update ptype_data to prototype_data

.github/workflows/tests.yml

@@ -78,11 +78,12 @@ 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
+            pip freeze > vetiver_requirements.txt
             docker build -t mock .
             docker run -d -v $PWD/pinsboard:/vetiver/pinsboard -p 8080:8080 mock
             sleep 5

README.md

@@ -43,7 +43,7 @@ from vetiver import mock, VetiverModel
 X, y = mock.get_mock_data()
 model = mock.get_mock_model().fit(X, y)
 
-v = VetiverModel(model, save_ptype=True, ptype_data=X, model_name='mock_model')
+v = VetiverModel(model, model_name='mock_model', prototype_data=X)
 ```
 
 You can **version** and **share** your `VetiverModel()` by choosing a [pins](https://rstudio.github.io/pins-python/) "board" for it, including a local folder, RStudio Connect, Amazon S3, and more.
@@ -60,7 +60,7 @@ You can **deploy** your pinned `VetiverModel()` using `VetiverAPI()`, an extensi
 
 ```python
 from vetiver import VetiverAPI
-app = VetiverAPI(v, check_ptype = True)
+app = VetiverAPI(v, check_prototype = True)
 ```
 
 To start a server using this object, use `app.run(port = 8080)` or your port of choice.

vetiver/__init__.py

@@ -2,7 +2,7 @@
 # Change to import.metadata when minimum python>=3.8
 from importlib_metadata import version as _version
 
-from .ptype import *  # noqa
+from .prototype import *  # noqa
 from .vetiver_model import VetiverModel  # noqa
 from .server import VetiverAPI, vetiver_endpoint, predict  # noqa
 from .mock import get_mock_data, get_mock_model  # noqa
@@ -19,6 +19,7 @@
 from .rsconnect import deploy_rsconnect  # noqa
 from .monitor import compute_metrics, pin_metrics, plot_metrics, _rolling_df  # noqa
 from .model_card import model_card  # noqa
+from .types import create_prototype, Prototype  # noqa
 
 __author__ = "Isabel Zimmerman <isabel.zimmerman@rstudio.com>"
 __all__ = []

vetiver/handlers/base.py

@@ -2,7 +2,7 @@
 from functools import singledispatch
 from contextlib import suppress
 
-from ..ptype import vetiver_create_ptype
+from ..prototype import vetiver_create_prototype
 from ..meta import _model_meta
 
 
@@ -20,14 +20,14 @@ def __init__(
 
 
 @singledispatch
-def create_handler(model, ptype_data):
+def create_handler(model, prototype_data):
     """check for model type to handle prediction
 
     Parameters
     ----------
     model: object
         Description of parameter `x`.
-    ptype_data : object
+    prototype_data : object
         An object with information (data) whose layout is to be determined.
 
     Returns
@@ -63,7 +63,7 @@ class BaseHandler:
     ----------
     model :
         a trained model
-    ptype_data :
+    prototype_data :
         An object with information (data) whose layout is to be determined.
     """
 
@@ -73,9 +73,9 @@ def __init_subclass__(cls, **kwargs):
         with suppress(AttributeError, NameError):
             create_handler.register(cls.model_class(), cls)
 
-    def __init__(self, model, ptype_data):
+    def __init__(self, model, prototype_data):
         self.model = model
-        self.ptype_data = ptype_data
+        self.prototype_data = prototype_data
 
     def describe(self):
         """Create description for model"""
@@ -93,21 +93,21 @@ def create_meta(
 
         return meta
 
-    def construct_ptype(self):
+    def construct_prototype(self):
         """Create data prototype for a model
 
         Parameters
         ----------
-        ptype_data : pd.DataFrame, np.ndarray, or None
-            Training data to create ptype
+        prototype_data : pd.DataFrame, np.ndarray, or None
+            Training data to create prototype
 
         Returns
         -------
-        ptype : pd.DataFrame or None
+        prototype : pd.DataFrame or None
             Zero-row DataFrame for storing data types
         """
-        ptype = vetiver_create_ptype(self.ptype_data)
-        return ptype
+        prototype = vetiver_create_prototype(self.prototype_data)
+        return prototype
 
     def handler_startup():
         """Include required packages for prediction
@@ -117,7 +117,7 @@ def handler_startup():
         """
         ...
 
-    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
@@ -128,8 +128,8 @@ def handler_predict(self, input_data, check_ptype):
         ----------
         input_data:
             Data used to generate prediction
-        check_ptype:
-            If type should be checked against `ptype` or not
+        check_prototype:
+            If type should be checked against `prototype` or not
 
         Returns
         -------
@@ -144,8 +144,8 @@ def handler_predict(self, input_data, check_ptype):
 
 
 @create_handler.register
-def _(model: base.BaseHandler, ptype_data):
-    if model.ptype_data is None and ptype_data is not None:
-        model.ptype_data = ptype_data
+def _(model: base.BaseHandler, prototype_data):
+    if model.prototype_data is None and prototype_data is not None:
+        model.prototype_data = prototype_data
 
     return model

vetiver/handlers/sklearn.py

@@ -33,7 +33,7 @@ def create_meta(
 
         return meta
 
-    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
@@ -51,7 +51,7 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
 
-        if not check_ptype or isinstance(input_data, pd.DataFrame):
+        if not check_prototype or isinstance(input_data, pd.DataFrame):
             prediction = self.model.predict(input_data)
         else:
             prediction = self.model.predict([input_data])

vetiver/handlers/statsmodels.py

@@ -21,9 +21,6 @@ class StatsmodelsHandler(BaseHandler):
 
     model_class = staticmethod(lambda: statsmodels.base.wrapper.ResultsWrapper)
 
-    def __init__(self, model, ptype_data):
-        super().__init__(model, ptype_data)
-
     def describe(self):
         """Create description for statsmodels model"""
         desc = f"Statsmodels {self.model.__class__} model."
@@ -41,7 +38,7 @@ def create_meta(
 
         return meta
 
-    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/handlers/torch.py

@@ -38,7 +38,7 @@ def create_meta(
 
         return meta
 
-    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
@@ -57,8 +57,8 @@ def handler_predict(self, input_data, check_ptype):
         """
         if not torch_exists:
             raise ImportError("Cannot import `torch`.")
-        if check_ptype:
-            input_data = np.array(input_data, dtype=np.array(self.ptype_data).dtype)
+        if check_prototype:
+            input_data = np.array(input_data, dtype=np.array(self.prototype_data).dtype)
             prediction = self.model(torch.from_numpy(input_data))
 
         # do not check ptype

vetiver/handlers/xgboost.py

@@ -38,7 +38,7 @@ def create_meta(
 
         return meta
 
-    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

@@ -38,7 +38,7 @@ def vetiver_pin_write(board, model: VetiverModel, versioned: bool = True):
     >>> model_board = board_temp(versioned = True, allow_pickle_read = True)
     >>> X, y = vetiver.get_mock_data()
     >>> model = vetiver.get_mock_model().fit(X, y)
-    >>> v = vetiver.VetiverModel(model = model, model_name = "my_model", ptype_data = X)
+    >>> v = vetiver.VetiverModel(model, "my_model", prototype_data = X)
     >>> vetiver.vetiver_pin_write(model_board, v)
     """
     if not board.allow_pickle_read:
@@ -51,14 +51,18 @@ def vetiver_pin_write(board, model: VetiverModel, versioned: bool = True):
         "with vetiver.model_card()",
     )
 
+    # convert older model's ptype to prototype
+    if hasattr(model, "ptype"):
+        model.prototype = model.ptype
+
     board.pin_write(
         model.model,
         name=model.model_name,
         type="joblib",
         description=model.description,
         metadata={
             "required_pkgs": model.metadata.get("required_pkgs"),
-            "ptype": None if model.ptype is None else model.ptype().json(),
+            "prototype": None if model.prototype is None else model.prototype().json(),
         },
         versioned=versioned,
     )