removing more references to ptype

Author: isabelizimm

.github/workflows/tests.yml

@@ -78,9 +78,9 @@ jobs:
             python-version: 3.8
         - name: Install dependencies
           run: |
-            python -m pip install --upgrade pip
-            python -m pip install -e ".[dev]"
-        - name: Run Docker
+            python -m pip install ".[dev]"
+            python -m pip install --upgrade git+https://github.com/rstudio/vetiver-python@${{ github.sha }}
+        - name: run Docker
           run: |
             python script/setup-docker/docker.py
             docker build -t mock .

vetiver/prototype.py

@@ -15,12 +15,13 @@
 
 class InvalidPTypeError(Exception):
     """
-    Throw an error if ptype cannot be recognised
+    Throw an error if prototype cannot be recognised
     """
 
     def __init__(
         self,
-        message="`ptype_data` must be a pd.DataFrame, a pydantic BaseModel or np.ndarray",
+        message="`prototype_data` must be a pd.DataFrame, a pydantic BaseModel, dict or "
+        "np.ndarray",
     ):
         self.message = message
         super().__init__(self.message)
@@ -33,14 +34,14 @@ def __init__(
 you should write a function to create the prototype. Here is \
 a template for such a function: \
 
-    from pydantic import create_model
-    from vetiver.prototype import vetiver_create_ptype
+    from vetiver.prototype import vetiver_create_prototype
+    from vetiver.types import create_prototype
 
-    @vetiver_create_ptype.register
+    @vetiver_create_prototype.register
     def _(data: {_data_type}):
         data_dict = ... # convert data to a dictionary
-        ptype = create_model("ptype", **data_dict)
-        return ptype
+        prototype = create_prototype(**data_dict)
+        return prototype
 
 If your datatype is a common type, please consider submitting \
 a pull request.
@@ -70,7 +71,7 @@ def vetiver_create_prototype(data):
 
     Returns
     -------
-    ptype : pydantic.main.BaseModel
+    prototype : vetiver.Prototype
         Data prototype
 
     """
@@ -91,17 +92,17 @@ def _(data: pd.DataFrame):
     --------
     >>> from pydantic import BaseModel
     >>> df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
-    >>> prototype = vetiver_create_ptype(df)
+    >>> prototype = vetiver_create_prototype(df)
     >>> issubclass(prototype, BaseModel)
     True
     >>> prototype()
-    ptype(x=1, y=4)
+    prototype(x=1, y=4)
 
     The data prototype created for the dataframe is equivalent to:
 
     >>> class another_prototype(BaseModel):
     ...     class Config:
-    ...         title = 'ptype'
+    ...         title = 'prototype'
     ...     x: int = 1
     ...     y: int = 4
 
@@ -134,14 +135,14 @@ def _(data: np.ndarray):
     Examples
     --------
     >>> arr = np.array([[1, 4], [2, 5], [3, 6]])
-    >>> prototype = vetiver_create_ptype(arr)
+    >>> prototype = vetiver_create_prototype(arr)
     >>> prototype()
-    ptype(0=1, 1=4)
+    prototype(0=1, 1=4)
 
     >>> arr2 = np.array([[1, 'a'], [2, 'b'], [3, 'c']], dtype=object)
-    >>> prototype2 = vetiver_create_ptype(arr2)
+    >>> prototype2 = vetiver_create_prototype(arr2)
     >>> prototype2()
-    ptype(0=1, 1='a')
+    prototype(0=1, 1='a')
     """
 
     def _item(value):

vetiver/tests/test_build_vetiver_model.py

@@ -13,6 +13,12 @@
 model = get_mock_model().fit(X_df, y)
 
 
+class MockPrototype(pydantic.BaseModel):
+    B: int
+    C: int
+    D: int
+
+
 def test_vetiver_model_array_prototype():
     vt1 = vt.VetiverModel(
         model=model,
@@ -24,6 +30,7 @@ def test_vetiver_model_array_prototype():
     )
 
     assert vt1.model == model
+    assert issubclass(vt1.prototype, vt.Prototype)
     assert isinstance(vt1.prototype.construct(), pydantic.BaseModel)
     assert list(vt1.prototype.__fields__.values())[0].type_ == int
 
@@ -59,6 +66,22 @@ def test_vetiver_model_dict_prototype():
     assert list(vt3.prototype.__fields__.values())[0].type_ == int
 
 
+def test_vetiver_model_basemodel_prototype():
+
+    m = MockPrototype(B=4, C=0, D=0)
+    vt4 = vt.VetiverModel(
+        model=model,
+        prototype_data=m,
+        model_name="model",
+        versioned=None,
+        description=None,
+        metadata=None,
+    )
+
+    assert vt4.model == model
+    assert vt4.prototype is m
+
+
 def test_vetiver_model_no_prototype():
     vt4 = vt.VetiverModel(
         model=model,

vetiver/vetiver_model.py

@@ -41,9 +41,9 @@ class VetiverModel:
 
     Attributes
     ----------
-    prototype : pydantic.main.BaseModel
+    prototype : vetiver.Prototype
         Data prototype
-    handler_predict:
+    handler_predict: Callable
         Method to make predictions from a trained model
 
     Notes