move to vetiver_create_prototype

Author: isabelizimm

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
 
 
@@ -106,7 +106,7 @@ def construct_prototype(self):
         prototype : pd.DataFrame or None
             Zero-row DataFrame for storing data types
         """
-        prototype = vetiver_create_ptype(self.prototype_data)
+        prototype = vetiver_create_prototype(self.prototype_data)
         return prototype
 
     def handler_startup():

vetiver/ptype.py vetiver/prototype.pyvetiver/ptype.py renamed to vetiver/prototype.py

@@ -8,21 +8,9 @@
 
 import pandas as pd
 import numpy as np
-from pydantic import BaseModel, create_model
-
-
-class NoAvailablePTypeError(Exception):
-    """
-    Throw an error if we cannot create
-    a 0 row input data prototype for `model`
-    """
-
-    def __init__(
-        self,
-        message="There is no method to create a 0-row input data prototype for `model`",
-    ):
-        self.message = message
-        super().__init__(self.message)
+import pydantic
+from warnings import warn
+from .types import create_prototype
 
 
 class InvalidPTypeError(Exception):
@@ -39,14 +27,14 @@ def __init__(
 
 
 CREATE_PTYPE_TPL = """\
-Failed to create a data prototype (ptype) from data of \
+Failed to create a data prototype from data of \
 type {_data_type}. If your datatype is not one of \
 (pd.DataFrame, pydantic.BaseModel, np.ndarry, dict), \
-you should write a function to create the ptype. Here is \
+you should write a function to create the prototype. Here is \
 a template for such a function: \
 
     from pydantic import create_model
-    from vetiver.ptype import vetiver_create_ptype
+    from vetiver.prototype import vetiver_create_ptype
 
     @vetiver_create_ptype.register
     def _(data: {_data_type}):
@@ -59,8 +47,20 @@ def _(data: {_data_type}):
 """
 
 
-@singledispatch
 def vetiver_create_ptype(data):
+
+    warn(
+        "argument for creating input data prototypes has changed to "
+        "vetiver_create_prototype, from vetiver_create_ptype",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    return vetiver_create_prototype(data)
+
+
+@singledispatch
+def vetiver_create_prototype(data):
     """Create zero row structure to save data types
 
     Parameters
@@ -77,7 +77,7 @@ def vetiver_create_ptype(data):
     raise InvalidPTypeError(message=CREATE_PTYPE_TPL.format(_data_type=type(data)))
 
 
-@vetiver_create_ptype.register
+@vetiver_create_prototype.register
 def _(data: pd.DataFrame):
     """
     Create input data prototype for a pandas dataframe
@@ -117,11 +117,11 @@ def _(data: pd.DataFrame):
     True
     """
     dict_data = data.iloc[0, :].to_dict()
-    ptype = create_model("ptype", **dict_data)
-    return ptype
+    prototype = create_prototype(**dict_data)
+    return prototype
 
 
-@vetiver_create_ptype.register
+@vetiver_create_prototype.register
 def _(data: np.ndarray):
     """
     Create input data prototype for a numpy array
@@ -156,11 +156,11 @@ def _item(value):
     dict_data = dict(enumerate(data[0], 0))
     # pydantic requires strings as indicies
     dict_data = {f"{key}": _item(value) for key, value in dict_data.items()}
-    ptype = create_model("ptype", **dict_data)
-    return ptype
+    prototype = create_prototype(**dict_data)
+    return prototype
 
 
-@vetiver_create_ptype.register
+@vetiver_create_prototype.register
 def _(data: dict):
     """
     Create input data prototype for a dict
@@ -170,11 +170,11 @@ def _(data: dict):
     data : dict
         Dictionary
     """
-    return create_model("ptype", **data)
+    return create_prototype(**data)
 
 
-@vetiver_create_ptype.register
-def _(data: BaseModel):
+@vetiver_create_prototype.register
+def _(data: pydantic.BaseModel):
     """
     Create input data prototype for a pydantic BaseModel object
 
@@ -186,7 +186,7 @@ def _(data: BaseModel):
     return data
 
 
-@vetiver_create_ptype.register
+@vetiver_create_prototype.register
 def _(data: NoneType):
     """
     Create input data prototype for None

vetiver/server.py

@@ -23,10 +23,10 @@ class VetiverAPI:
         Model to be deployed in API
     check_prototype : bool
         Determine if data prototype should be enforced
-    check_ptype : bool
-        Deprecated in favor of check_prototype
     app_factory :
         Type of API to be deployed
+    **kwargs: dict
+        Deprecated parameters.
 
     Example
     -------
@@ -35,6 +35,11 @@ class VetiverAPI:
     >>> model = vt.get_mock_model().fit(X, y)
     >>> v = vt.VetiverModel(model = model, model_name = "my_model", prototype_data = X)
     >>> v_api = vt.VetiverAPI(model = v, check_prototype = True)
+
+    Notes
+    -----
+    Parameter `check_ptype` was changed to `check_prototype`. Handling of `check_ptype`
+    will be removed in a future version.
     """
 
     app = None

vetiver/types.py

@@ -0,0 +1,11 @@
+from pydantic import BaseModel, create_model
+
+all = ["Prototype", "create_prototype"]
+
+
+class Prototype(BaseModel):
+    pass
+
+
+def create_prototype(**dict_data):
+    return create_model("prototype", __base__=Prototype, **dict_data)

vetiver/vetiver_model.py

@@ -29,19 +29,19 @@ class VetiverModel:
         Model name or ID
     prototype_data : pd.DataFrame, np.array
         Sample of data model should expect when it is being served
-    ptype_data : pd.DataFrame, np.array
-        Deprecated, in favor of prototype_data
     versioned :
         Should the model be versioned when created?
     description : str
         A detailed description of the model.
         If omitted, a brief description will be generated.
     metadata : dict
         Other details to be saved and accessed for serving
+    **kwargs: dict
+        Deprecated parameters.
 
     Attributes
     ----------
-    ptype : pydantic.main.BaseModel
+    prototype : pydantic.main.BaseModel
         Data prototype
     handler_predict:
         Method to make predictions from a trained model
@@ -50,6 +50,10 @@ class VetiverModel:
     -----
     VetiverModel can also take an initialized custom VetiverHandler
     as a model, for advanced use cases or non-supported model types.
+    Parameter `ptype_data` was changed to `prototype_data`. Handling of `ptype_data`
+    will be removed in a future version.
+
+
 
     Example
     -------