Remove save_ptype as a parameter and as a property

save_ptype is still used in the test helper functions,
but it is not part of the API anymore.

Author: has2k1

examples/coffeeratings.py

@@ -17,7 +17,7 @@
 lr_fit = LinearRegression().fit(X_train, y_train)
 
 # create vetiver model
-v = vetiver.VetiverModel(lr_fit, save_ptype = True, ptype_data=X_train, model_name = "v")
+v = vetiver.VetiverModel(lr_fit, ptype_data=X_train, model_name = "v")
 
 # version model via pin
 from pins import board_folder

vetiver/handlers/_interface.py

@@ -7,7 +7,7 @@
 except ImportError:
     torch_exists = False
 
-def create_translator(model, ptype_data, save_ptype):
+def create_translator(model, ptype_data):
     """check for model type to handle prediction
 
     Parameters
@@ -22,10 +22,10 @@ def create_translator(model, ptype_data, save_ptype):
     """
     if torch_exists:
         if isinstance(model, torch.nn.Module):
-            return pytorch_vt.TorchHandler(model, ptype_data, save_ptype)
+            return pytorch_vt.TorchHandler(model, ptype_data)
 
     if isinstance(model, sklearn.base.BaseEstimator):
-        return sklearn_vt.SKLearnHandler(model, ptype_data, save_ptype)
+        return sklearn_vt.SKLearnHandler(model, ptype_data)
 
     else:
         raise NotImplementedError

vetiver/handlers/pytorch_vt.py

@@ -17,10 +17,9 @@ class TorchHandler:
     model : nn.Module
         a trained torch model
     """
-    def __init__(self, model, ptype_data, save_ptype):
+    def __init__(self, model, ptype_data):
         self.model = model
         self.ptype_data = ptype_data
-        self.save_ptype = save_ptype
 
     def create_description(self):
         """Create description for torch model
@@ -48,14 +47,13 @@ def ptype(self):
         ----------
         ptype_data : pd.DataFrame, np.ndarray, or None
             Training data to create ptype
-        save_ptype : bool
 
         Returns
         -------
         ptype : pd.DataFrame or None
             Zero-row DataFrame for storing data types
         """
-        ptype = vetiver_create_ptype(self.ptype_data, self.save_ptype)
+        ptype = vetiver_create_ptype(self.ptype_data)
 
         return ptype
 

vetiver/handlers/sklearn_vt.py

@@ -12,10 +12,9 @@ class SKLearnHandler:
         a trained sklearn model
     """
 
-    def __init__(self, model, ptype_data, save_ptype):
+    def __init__(self, model, ptype_data):
         self.model = model
         self.ptype_data = ptype_data
-        self.save_ptype = save_ptype
 
     def create_description(self):
         """Create description for sklearn model
@@ -42,14 +41,13 @@ def ptype(self):
         ----------
         ptype_data : pd.DataFrame, np.ndarray, or None
             Training data to create ptype
-        save_ptype : bool
 
         Returns
         -------
         ptype : pd.DataFrame or None
             Zero-row DataFrame for storing data types
         """
-        ptype = vetiver_create_ptype(self.ptype_data, self.save_ptype)
+        ptype = vetiver_create_ptype(self.ptype_data)
         return ptype
 
     def handler_startup():

vetiver/pin_read_write.py

@@ -29,7 +29,6 @@ def vetiver_pin_write(board, model: VetiverModel, versioned: bool=True):
         type = "joblib",
         description = model.description,
         metadata = {"required_pkgs": model.metadata.get("required_pkgs"),
-                    "save_ptype": model.save_ptype,
                     "ptype": None if model.ptype == None else model.ptype().json()},
         versioned=versioned
     )
@@ -79,7 +78,6 @@ def vetiver_pin_read(board, name: str, version: str = None) -> VetiverModel:
              url = meta.user.get("url"), # None all the time, besides Connect
              required_pkgs = meta.user.get("required_pkgs")
         ),
-        save_ptype=meta.user.get("save_ptype"),
         ptype_data = json.loads(meta.user.get("ptype")) if meta.user.get("ptype") else None,
         versioned = True
         )

vetiver/ptype.py

@@ -22,13 +22,12 @@ def __init__(
 
 class InvalidPTypeError(Exception):
     """
-    Throw an error if `save_ptype` is not
-    True, False, or data.frame
+    Throw an error if ptype cannot be recognised
     """
 
     def __init__(
         self,
-        message="The `ptype_data` argument must be a pandas.DataFrame, a pydantic BaseModel,  np.ndarray, or `save_ptype` must be FALSE.",
+        message="`ptype_data` must be a pd.DataFrame, a pydantic BaseModel or np.ndarray",
     ):
         self.message = message
         super().__init__(self.message)
@@ -55,7 +54,7 @@ def _(data: {_data_type}):
 """
 
 @singledispatch
-def vetiver_create_ptype(data, save_ptype):
+def vetiver_create_ptype(data):
     """Create zero row structure to save data types
 
     Parameters
@@ -69,20 +68,20 @@ def vetiver_create_ptype(data, save_ptype):
         Data prototype
 
     """
-    msg = CREATE_PTYPE_TPL.format(_data_type=type(data))
-    msg = ""
-    raise InvalidPTypeError(message=msg)
+    raise InvalidPTypeError(
+        message=CREATE_PTYPE_TPL.format(_data_type=type(data))
+    )
 
 
 @vetiver_create_ptype.register
-def _vetiver_create_ptype(data: pd.DataFrame, save_ptype):
+def _vetiver_create_ptype(data: pd.DataFrame):
     dict_data = data.iloc[1, :].to_dict()
     ptype = create_model("ptype", **dict_data)
     return ptype
 
 
 @vetiver_create_ptype.register
-def _vetiver_create_ptype(data: np.ndarray, save_ptype):
+def _vetiver_create_ptype(data: np.ndarray):
     dict_data = dict(enumerate(data[1], 0))
     # pydantic requires strings as indicies
     dict_data = {f"{key}": value.item() for key, value in dict_data.items()}
@@ -91,15 +90,15 @@ def _vetiver_create_ptype(data: np.ndarray, save_ptype):
 
 
 @vetiver_create_ptype.register
-def _vetiver_create_ptype(data: dict, save_ptype):
+def _vetiver_create_ptype(data: dict):
     return create_model("ptype", **data)
 
 
 @vetiver_create_ptype.register
-def _vetiver_create_ptype(data: BaseModel, save_ptype):
+def _vetiver_create_ptype(data: BaseModel):
     return data
 
 
 @vetiver_create_ptype.register
-def _vetiver_create_ptype(data: NoneType, save_ptype):
+def _vetiver_create_ptype(data: NoneType):
     return None

vetiver/tests/test_add_endpoint.py

@@ -9,7 +9,6 @@ def _start_application(check_ptype):
     model = mock.get_mock_model().fit(X, y)
     v = VetiverModel(
         model=model,
-        save_ptype=True,
         ptype_data=X,
         model_name="my_model",
         versioned=None,
@@ -43,4 +42,4 @@ def test_endpoint_adds_no_ptype():
     data = [0,0,0]
     response = client.post("/sum/", json=data)
     assert response.status_code == 200, response.text
-    assert response.json() == {"sum": 0}, response.json()
+    assert response.json() == {"sum": 0}, response.json()

vetiver/tests/test_build_api.py

@@ -9,7 +9,6 @@ def _build_v():
     model = mock.get_mock_model().fit(X, y)
     v = VetiverModel(
         model=model,
-        save_ptype=True,
         ptype_data=X,
         model_name="my_model",
         versioned=None,

vetiver/tests/test_build_vetiver_model.py

@@ -16,7 +16,6 @@ def test_vetiver_model_array_ptype():
     # build VetiverModel, no ptype
     vt1 = VetiverModel(
         model=model,
-        save_ptype=True,
         ptype_data=X_array,
         model_name="iris",
         versioned=None,
@@ -32,7 +31,6 @@ def test_vetiver_model_df_ptype():
     # build VetiverModel, df ptype_data
     vt2 = VetiverModel(
         model=model,
-        save_ptype=True,
         ptype_data=X_df,
         model_name="iris",
         versioned=None,
@@ -48,7 +46,6 @@ def test_vetiver_model_dict_ptype():
     dict_data = {"B": 0, "C": 0, "D": 0}
     vt3 = VetiverModel(
         model=model,
-        save_ptype=True,
         ptype_data=dict_data,
         model_name="iris",
         versioned=None,
@@ -64,8 +61,7 @@ def test_vetiver_model_no_ptype():
     # build VetiverModel, no ptype
     vt4 = VetiverModel(
         model=model,
-        save_ptype=False,
-        ptype_data=X_df,
+        ptype_data=None,
         model_name="iris",
         versioned=None,
         description=None,
@@ -74,17 +70,3 @@ def test_vetiver_model_no_ptype():
 
     assert vt4.model == model
     assert vt4.ptype == None
-
-
-def test_vetiver_model_error():
-    with pytest.raises(AttributeError):
-        VetiverModel(
-        model=model,
-        save_ptype=True,
-        ptype_data=None,
-        model_name="iris",
-        versioned=None,
-        description=None,
-        metadata=None,
-    )
-

vetiver/tests/test_no_handler.py

@@ -8,7 +8,6 @@ def test_not_implemented_error():
     with pytest.raises(NotImplementedError):
         VetiverModel(
         model=y,
-        save_ptype=True,
         ptype_data=X,
         model_name="my_model",
         versioned=None,