Merge pull request #36 from isabelizimm/docs-cleanup

refactor docstrings for clarity

Author: isabelizimm

vetiver/handlers/pytorch_vt.py

@@ -1,5 +1,5 @@
 from ..meta import vetiver_meta
-from ..ptype import _vetiver_create_ptype
+from ..ptype import vetiver_create_ptype
 import numpy as np
 
 torch_exists = True
@@ -55,7 +55,7 @@ def ptype(self):
         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, self.save_ptype)
 
         return ptype
 

vetiver/handlers/sklearn_vt.py

@@ -1,8 +1,7 @@
-from ..ptype import _vetiver_create_ptype
+from ..ptype import vetiver_create_ptype
 import pandas as pd
 from ..meta import vetiver_meta
 import sklearn
-import numpy as np
 
 class SKLearnHandler:
     """Handler class for creating VetiverModels with sklearn.
@@ -50,7 +49,7 @@ def ptype(self):
         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, self.save_ptype)
         return ptype
 
     def handler_startup():

vetiver/pin_read_write.py

@@ -9,12 +9,12 @@
 
 def vetiver_pin_write(board, model: VetiverModel, versioned: bool=True):
     """
-    Write pin including VetiverModel
+    Pin a trained VetiverModel along with other model metadata.
     
     Parameters
     ----------
-    board: pins.BaseBoard
-        Location for pin to be saved
+    board:
+        A pin board, created by `pins.board_folder()` or another `board_` function.
     model: vetiver.VetiverModel
         VetiverModel to be written to board
     versioned: bool
@@ -50,16 +50,20 @@ def vetiver_pin_read(board, name: str, version: str = None) -> VetiverModel:
     
     Parameters
     ----------
-    board: pins.BaseBoard
-        Board where pin is held
+    board:
+        A pin board, created by `pins.board_folder()` or another `board_` function.
     name: string
-        Name of pin
-    versioned: bool
-        Whether or not the pin should be versioned
+        Pin name
+    version: str
+        Retrieve a specific version of a pin. 
 
     Returns
     --------
     vetiver.VetiverModel
+
+    Notes
+    -----
+    If reading a board from RSConnect, the `board` argument must be in "username/modelname" format.
     
     """
     version = version if version is not None else _choose_version(board.pin_versions(name))

vetiver/ptype.py

@@ -1,8 +1,6 @@
 import pandas as pd
 import numpy as np
 from pydantic import BaseModel, create_model
-from uvicorn import Config
-
 
 class NoAvailablePTypeError(Exception):
     """
@@ -32,7 +30,7 @@ def __init__(
         super().__init__(self.message)
 
 
-def _vetiver_create_ptype(ptype_data, save_ptype: bool):
+def vetiver_create_ptype(ptype_data, save_ptype: bool):
     """Create zero row structure to save data types
     Parameters
     ----------

vetiver/server.py

@@ -22,10 +22,6 @@ class VetiverAPI:
         Model to be deployed in API
     check_ptype : bool
         Determine if data prototype should be enforced
-    port :  int
-        Port for deployment
-    host :
-        Host address
     app_factory :
         Type of API to be deployed
     app :
@@ -153,7 +149,16 @@ def run(self,
         port: int = 8000,
         host: str = "127.0.0.1",
         **kw):
-        """Start API"""
+        """
+       Start API
+        
+        Parameters
+        ----------
+        port : int
+            An integer that indicates the server port that should be listened on.
+        host : str
+            A valid IPv4 or IPv6 address, which the application will listen on. 
+        """
         _jupyter_nb()
         uvicorn.run(self.app, port=port, host=host, **kw)
 
@@ -177,13 +182,13 @@ def predict(endpoint, data: Union[dict, pd.DataFrame, pd.Series], **kw):
     ----------
     endpoint :
         URI path to endpoint
-    data : dict
+    data : Union[dict, pd.DataFrame, pd.Series]
         Name of endpoint
 
     Returns
     -------
     dict
-        Key: endpoint_name Value: Output of endpoint_fx, in list format
+        Endpoint_name and list of endpoint_fx output
     """
     if isinstance(endpoint, testclient.TestClient):
         requester = endpoint