check_ptype to check_prototype

Author: isabelizimm

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
@@ -57,7 +57,7 @@ def handler_predict(self, input_data, check_ptype):
         """
         if not torch_exists:
             raise ImportError("Cannot import `torch`.")
-        if check_ptype:
+        if check_prototype:
             input_data = np.array(input_data, dtype=np.array(self.ptype_data).dtype)
             prediction = self.model(torch.from_numpy(input_data))
 

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/server.py

@@ -1,4 +1,4 @@
-from typing import Callable, List, Union, Any, Dict
+from typing import Any, Callable, Dict, List, Union
 from urllib.parse import urljoin
 
 import httpx
@@ -8,6 +8,7 @@
 from fastapi import FastAPI, Request, testclient
 from fastapi.openapi.utils import get_openapi
 from fastapi.responses import HTMLResponse, RedirectResponse
+from warnings import warn
 
 from .utils import _jupyter_nb
 from .vetiver_model import VetiverModel
@@ -20,8 +21,10 @@ class VetiverAPI:
     ----------
     model :  VetiverModel
         Model to be deployed in API
-    check_ptype : bool
+    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
 
@@ -31,22 +34,32 @@ class VetiverAPI:
     >>> X, y = vt.get_mock_data()
     >>> 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_ptype = True)
+    >>> v_api = vt.VetiverAPI(model = v, check_prototype = True)
     """
 
     app = None
 
     def __init__(
         self,
         model: VetiverModel,
-        check_ptype: bool = True,
+        check_prototype: bool = True,
+        check_ptype: bool = None,
         app_factory=FastAPI,
     ) -> None:
         self.model = model
-        self.check_ptype = check_ptype
         self.app_factory = app_factory
         self.app = app_factory()
 
+        if check_ptype is not None:
+            check_prototype = check_ptype
+            warn(
+                "argument for checking input data prototype has changed to "
+                "check_prototype, from check_ptype",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        self.check_prototype = check_prototype
+
         self._init_app()
 
     def _init_app(self):
@@ -71,7 +84,7 @@ async def ping():
             return {"ping": "pong"}
 
         self.vetiver_post(
-            self.model.handler_predict, "predict", check_ptype=self.check_ptype
+            self.model.handler_predict, "predict", check_prototype=self.check_prototype
         )
 
         @app.get("/__docs__", response_class=HTMLResponse, include_in_schema=False)
@@ -130,15 +143,15 @@ def vetiver_post(self, endpoint_fx: Callable, endpoint_name: str = None, **kw):
         >>> X, y = vt.get_mock_data()
         >>> model = vt.get_mock_model().fit(X, y)
         >>> v = vt.VetiverModel(model = model, model_name = "model", prototype_data = X)
-        >>> v_api = vt.VetiverAPI(model = v, check_ptype = True)
+        >>> v_api = vt.VetiverAPI(model = v, check_prototype = True)
         >>> def sum_values(x):
         ...     return x.sum()
         >>> v_api.vetiver_post(sum_values, "sums")
         """
         if not endpoint_name:
             endpoint_name = endpoint_fx.__name__
 
-        if self.check_ptype is True:
+        if self.check_prototype is True:
 
             @self.app.post(urljoin("/", endpoint_name), name=endpoint_name)
             async def custom_endpoint(
@@ -179,7 +192,7 @@ def run(self, port: int = 8000, host: str = "127.0.0.1", **kw):
         >>> X, y = vt.get_mock_data()
         >>> model = vt.get_mock_model().fit(X, y)
         >>> v = vt.VetiverModel(model = model, model_name = "model", prototype_data = X)
-        >>> v_api = vt.VetiverAPI(model = v, check_ptype = True)
+        >>> v_api = vt.VetiverAPI(model = v, check_prototype = True)
         >>> v_api.run()     # doctest: +SKIP
         """
         _jupyter_nb()

vetiver/tests/test_add_endpoint.py

@@ -34,9 +34,9 @@ def sum_dict(x):
 
 
 @pytest.fixture
-def vetiver_client(vetiver_model):  # With check_ptype=True
+def vetiver_client(vetiver_model):  # With check_prototype=True
 
-    app = VetiverAPI(vetiver_model, check_ptype=True)
+    app = VetiverAPI(vetiver_model, check_prototype=True)
     app.vetiver_post(sum_values, "sum")
 
     app.app.root_path = "/sum"
@@ -46,9 +46,9 @@ def vetiver_client(vetiver_model):  # With check_ptype=True
 
 
 @pytest.fixture
-def vetiver_client_check_ptype_false(vetiver_model):  # With check_ptype=False
+def vetiver_client_check_ptype_false(vetiver_model):  # With check_prototype=False
 
-    app = VetiverAPI(vetiver_model, check_ptype=False)
+    app = VetiverAPI(vetiver_model, check_prototype=False)
     app.vetiver_post(sum_dict, "sum")
 
     app.app.root_path = "/sum"

vetiver/tests/test_predict.py

@@ -27,7 +27,7 @@ def vetiver_model():
 
 @pytest.fixture
 def vetiver_client(vetiver_model):  # With check_ptype=True
-    app = VetiverAPI(vetiver_model, check_ptype=True)
+    app = VetiverAPI(vetiver_model, check_prototype=True)
     app.app.root_path = "/predict"
     client = TestClient(app.app)
 
@@ -36,7 +36,7 @@ def vetiver_client(vetiver_model):  # With check_ptype=True
 
 @pytest.fixture
 def vetiver_client_check_ptype_false(vetiver_model):  # With check_ptype=False
-    app = VetiverAPI(vetiver_model, check_ptype=False)
+    app = VetiverAPI(vetiver_model, check_prototype=False)
     app.app.root_path = "/predict"
     client = TestClient(app.app)
 
@@ -90,7 +90,7 @@ def test_predict_sklearn_type_error(data, vetiver_client):
 
 def test_predict_server_error(vetiver_model):
     X, y = mock.get_mock_data()
-    app = VetiverAPI(vetiver_model, check_ptype=True)
+    app = VetiverAPI(vetiver_model, check_prototype=True)
     app.app.root_path = "/i_do_not_exists"
     client = TestClient(app.app)
 

vetiver/tests/test_pytorch.py

@@ -102,7 +102,7 @@ def test_torch_predict_no_ptype_batch():
     torch.manual_seed(3)
     x_train, torch_model = _build_torch_v()
     v = VetiverModel(torch_model, model_name="torch")
-    v_api = VetiverAPI(v, check_ptype=False)
+    v_api = VetiverAPI(v, check_prototype=False)
 
     client = TestClient(v_api.app)
     data = [[3.3], [3.3]]
@@ -117,7 +117,7 @@ def test_torch_predict_no_ptype():
     torch.manual_seed(3)
     x_train, torch_model = _build_torch_v()
     v = VetiverModel(torch_model, model_name="torch")
-    v_api = VetiverAPI(v, check_ptype=False)
+    v_api = VetiverAPI(v, check_prototype=False)
 
     client = TestClient(v_api.app)
     data = [[3.3]]

vetiver/tests/test_sklearn.py

@@ -15,7 +15,7 @@ def _start_application(save_ptype: bool = True):
         description="A regression model for testing purposes",
     )
 
-    app = VetiverAPI(v, check_ptype=save_ptype)
+    app = VetiverAPI(v, check_prototype=save_ptype)
 
     return app
 

vetiver/tests/test_statsmodels.py

@@ -24,17 +24,17 @@ def sm_model():
 
 
 @pytest.fixture
-def vetiver_client(sm_model):  # With check_ptype=True
-    app = vetiver.VetiverAPI(sm_model, check_ptype=True)
+def vetiver_client(sm_model):  # With check_prototype=True
+    app = vetiver.VetiverAPI(sm_model, check_prototype=True)
     app.app.root_path = "/predict"
     client = TestClient(app.app)
 
     return client
 
 
 @pytest.fixture
-def vetiver_client_check_ptype_false(sm_model):  # With check_ptype=True
-    app = vetiver.VetiverAPI(sm_model, check_ptype=False)
+def vetiver_client_check_ptype_false(sm_model):  # With check_prototype=True
+    app = vetiver.VetiverAPI(sm_model, check_prototype=False)
     app.app.root_path = "/predict"
     client = TestClient(app.app)