linitng and trying out single source version based on git

bingo/__init__.py

@@ -13,5 +13,3 @@
 License for the specific language governing permissions and limitations under
 the License.
 """
-
-__version__ = "0.5.0"

bingo/symbolic_regression/agraph/onnx_interface.py

@@ -1,3 +1,9 @@
+"""Wrapper around ONNX for deploying bingo equations
+
+This module is a wrapper around the onnx package that allows for conversion of
+bingo AGraph objects to the onnx standard.
+"""
+
 import numpy as np
 from onnx import numpy_helper, TensorProto
 from onnx.helper import (
@@ -49,18 +55,32 @@
 
 
 def make_onnx_model(command_array, constants, name="bingo_equation"):
+    """creates an onnx model from bingo agraph represntation
+
+    Parameters
+    ----------
+    command_array : Nx3 array of int
+        acyclic graph stack
+    constants : tuple of numeric
+        numeric constants that are used in the equation
+    name : str, optional
+        name for onnx model, by default "bingo_equation"
+
+    Returns
+    -------
+    onnx Model
+        onnx represntation of the AGraph
+    """
 
     nodes = []
     slice_inds = set()
 
-    X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
-    Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
-    C = numpy_helper.from_array(np.array(constants), name="C")
+    input_ = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
+    output = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
+    initializer = numpy_helper.from_array(np.array(constants), name="C")
     nodes.append(make_node("Constant", [], ["ax0"], value_ints=[0]))
     nodes.append(make_node("Constant", [], ["ax1"], value_ints=[1]))
 
-    Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
-
     for i, (op, p1, p2) in enumerate(command_array):
         output_name = f"O{i}" if i < len(command_array) - 1 else "Y"
         if op == INTEGER:
@@ -85,15 +105,14 @@ def make_onnx_model(command_array, constants, name="bingo_equation"):
             )
         elif op in ABS_SAFETY:
             nodes.append(make_node("Abs", [f"O{p1}"], [f"{output_name}aux"]))
-            inputs = (
+            inps = (
                 [f"{output_name}aux", f"O{p2}"]
                 if IS_ARITY_2_MAP[op]
                 else [f"{output_name}aux"]
             )
-            nodes.append(make_node(ONNX_FUNCTIONS[op], inputs, [output_name]))
+            nodes.append(make_node(ONNX_FUNCTIONS[op], inps, [output_name]))
         else:
-            inputs = [f"O{p1}", f"O{p2}"] if IS_ARITY_2_MAP[op] else [f"O{p1}"]
-            nodes.append(make_node(ONNX_FUNCTIONS[op], inputs, [output_name]))
+            inps = [f"O{p1}", f"O{p2}"] if IS_ARITY_2_MAP[op] else [f"O{p1}"]
+            nodes.append(make_node(ONNX_FUNCTIONS[op], inps, [output_name]))
 
-    graph = make_graph(nodes, name, [X], [Y], [C])  # inputs  # outputs  # initializers
-    return make_model(graph)
+    return make_model(make_graph(nodes, name, [input_], [output], [initializer]))

pyproject.toml

@@ -5,10 +5,13 @@ requires = [
     "ninja",
     "cmake>=3.12",
     "pybind11[global]",
+    "setuptools-scm>=8",
 ]
 build-backend = "setuptools.build_meta"
 
 [project]
+name = "bingo"
+dynamic = ["version"]
 dependencies = [
     "mpi4py>=2.0.0,<4.0",
     "numpy",
@@ -32,4 +35,6 @@ TESTS = [
 ]
 
 [tool.cibuildwheel]
-skip = "pp*"
+skip = "pp*"
+
+[tool.setuptools_scm]

setup.py

@@ -18,17 +18,6 @@
 }
 
 
-def get_property(prop, project):
-    result = re.search(
-        r'{}\s*=\s*[\'"]([^\'"]*)[\'"]'.format(prop),
-        open(project + "/__init__.py").read(),
-    )
-    return result.group(1)
-
-
-__version__ = get_property("__version__", "bingo")
-
-
 class CMakeExtension(Extension):
     def __init__(self, name, sourcedir=""):
         Extension.__init__(self, name, sources=[])
@@ -140,7 +129,6 @@ def build_extension(self, ext: CMakeExtension) -> None:
 
 setup(
     name="bingo-nasa",
-    version=__version__,
     author="Geoffrey Bomarito",
     author_email="[email protected]",
     description="A package for genetic optimization and symbolic regression",