refactor metadata w prototypes

Author: isabelizimm

docs/source/advancedusage/custom_handler.md

@@ -12,7 +12,6 @@ class CustomHandler(BaseHandler):
         super().__init__(model, ptype_data)
 
     model_type = staticmethod(lambda: newmodeltype)
-    pkg = sklearn # modeling package
     pip_name = "scikit-learn" # pkg name on pip, used for tracking pkg versions
 
     def handler_predict(self, input_data, check_ptype: bool):

script/setup-docker/docker.py

@@ -13,5 +13,4 @@
 
 vetiver.vetiver_pin_write(board, v)
 
-
 vetiver.prepare_docker(board, "mymodel")

vetiver/handlers/sklearn.py

@@ -16,7 +16,7 @@ class SKLearnHandler(BaseHandler):
     model_class = staticmethod(lambda: sklearn.base.BaseEstimator)
     pip_name = "scikit-learn"
 
-    def handler_predict(self, input_data, check_ptype):
+    def handler_predict(self, input_data, check_prototype):
         """Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this

vetiver/pin_read_write.py

@@ -55,6 +55,7 @@ def vetiver_pin_write(board, model: VetiverModel, versioned: bool = True):
     # convert older model's ptype to prototype
     if hasattr(model, "ptype"):
         model.prototype = model.ptype
+        delattr(model, "ptype")
     # metadata is dict
     if isinstance(model.metadata, dict):
         model.metadata = VetiverMeta.from_dict(model.metadata)
@@ -68,7 +69,7 @@ def vetiver_pin_write(board, model: VetiverModel, versioned: bool = True):
             "user": model.metadata.user,
             "vetiver_meta": {
                 "required_pkgs": model.metadata.required_pkgs,
-                "ptype": None if model.prototype is None else model.prototype().json(),
+                "prototype": None if not model.prototype else model.prototype().json(),
             },
         },
         versioned=versioned,

vetiver/tests/test_build_vetiver_model.py

@@ -75,7 +75,7 @@ def test_vetiver_model_basemodel_prototype():
         prototype_data=m,
         model_name="model",
         versioned=False,
-        description=None
+        description=None,
     )
 
     assert vt4.model == model
@@ -107,7 +107,7 @@ def test_vetiver_model_use_ptype():
     )
 
     assert vt5.model == model
-    assert vt5.ptype is None
+    assert vt5.prototype is None
     assert vt5.metadata == VetiverMeta(
         user={"test": 123},
         version=None,
@@ -134,8 +134,8 @@ def test_vetiver_model_from_pin():
     assert isinstance(v2, vt.VetiverModel)
     assert isinstance(v2.model, sklearn.base.BaseEstimator)
     assert isinstance(v2.prototype.construct(), pydantic.BaseModel)
-    assert v2.metadata.get("user") == {"test": 123}
-    assert v2.metadata.get("version") is not None
+    assert v2.metadata.user == {"test": 123}
+    assert v2.metadata.version is not None
     assert v2.metadata.required_pkgs == ["scikit-learn"]
-    
+
     board.pin_delete("model")

vetiver/vetiver_model.py

@@ -100,11 +100,18 @@ def from_pin(cls, board, name: str, version: str = None):
         meta = board.pin_meta(name, version)
 
         if "vetiver_meta" in meta.user:
-            ptype = meta.user.get("vetiver_meta").get("prototype")
-            required_pkgs = meta.user.get("vetiver_meta").get("required_pkgs")
+            get_prototype = meta.user.get("vetiver_meta").get("prototype", None)
+            required_pkgs = meta.user.get("vetiver_meta").get("required_pkgs", None)
             meta.user.pop("vetiver_meta")
         else:
-            ptype = meta.user.get("ptype", None)
+            # ptype = meta.user.get("ptype", None)
+
+            get_prototype = meta.user.get("ptype")
+            # elif meta.user.get("prototype"):
+            #     get_prototype = meta.user.get("prototype")
+            # else:
+            #     get_prototype = None
+
             required_pkgs = meta.user.get("required_pkgs")
 
         return cls(