more clear naming in handlers

Author: isabelizimm

vetiver/handlers/base.py

@@ -18,12 +18,12 @@ def __init__(self, model, ptype_data):
         self.ptype_data = ptype_data
 
 
-    def create_description(self):
+    def describe(self):
         """Create description for model"""
         desc = f"{self.model.__class__} model"
         return desc
 
-    def vetiver_create_meta(
+    def create_meta(
         user: list = None,
         version: str = None,
         url: str = None,
@@ -34,7 +34,7 @@ def vetiver_create_meta(
 
         return meta
 
-    def ptype(self):
+    def construct_ptype(self):
         """Create data prototype for torch model
 
         Parameters

vetiver/handlers/sklearn.py

@@ -1,10 +1,9 @@
-from vetiver.handlers.base import VetiverHandler
-from ..ptype import vetiver_create_ptype
-from ..meta import vetiver_meta
-
 import pandas as pd
 import sklearn
 
+from .base import VetiverHandler
+from ..meta import vetiver_meta
+
 class SKLearnHandler(VetiverHandler):
     """Handler class for creating VetiverModels with sklearn.
 
@@ -18,13 +17,13 @@ class SKLearnHandler(VetiverHandler):
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
-    def create_description(self):
+    def describe(self):
         """Create description for sklearn model
         """
         desc = f"Scikit-learn {self.model.__class__} model"
         return desc
 
-    def vetiver_create_meta(
+    def construct_meta(
         user: list = None,
         version: str = None,
         url: str = None,
@@ -36,30 +35,6 @@ def vetiver_create_meta(
 
         return meta
 
-    def ptype(self):
-        """Create data prototype for torch model
-
-        Parameters
-        ----------
-        ptype_data : pd.DataFrame, np.ndarray, or None
-            Training data to create ptype
-
-        Returns
-        -------
-        ptype : pd.DataFrame or None
-            Zero-row DataFrame for storing data types
-        """
-        ptype = vetiver_create_ptype(self.ptype_data)
-        return ptype
-
-    def handler_startup():
-        """Include required packages for prediction
-
-        The `handler_startup` function executes when the API starts. Use this
-        function for tasks like loading packages.
-        """
-        ...
-
 
     def handler_predict(self, input_data, check_ptype):
         """Generates method for /predict endpoint in VetiverAPI

vetiver/handlers/torch.py

@@ -1,8 +1,8 @@
-from vetiver.handlers.base import VetiverHandler
-from ..meta import vetiver_meta
-from ..ptype import vetiver_create_ptype
 import numpy as np
 
+from .base import VetiverHandler
+from ..meta import vetiver_meta
+
 torch_exists = True
 try:
     import torch
@@ -22,13 +22,13 @@ class TorchHandler(VetiverHandler):
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
-    def create_description(self):
+    def describe(self):
         """Create description for torch model
         """
         desc = f"Pytorch model of type {type(self.model)}"
         return desc
 
-    def vetiver_create_meta(
+    def create_meta(
         user: list = None,
         version: str = None,
         url: str = None,
@@ -41,31 +41,6 @@ def vetiver_create_meta(
 
         return meta
 
-    def ptype(self):
-        """Create data prototype for torch model
-
-        Parameters
-        ----------
-        ptype_data : pd.DataFrame, np.ndarray, or None
-            Training data to create ptype
-
-        Returns
-        -------
-        ptype : pd.DataFrame or None
-            Zero-row DataFrame for storing data types
-        """
-        ptype = vetiver_create_ptype(self.ptype_data)
-
-        return ptype
-
-    def handler_startup():
-        """Include required packages for prediction
-
-        The `handler_startup` function executes when the API starts. Use this
-        function for tasks like loading packages.
-        """
-        ...
-
     def handler_predict(self, input_data, check_ptype):
         """Generates method for /predict endpoint in VetiverAPI
 

vetiver/vetiver_model.py

@@ -60,11 +60,11 @@ def __init__(
         translator = create_handler(model, ptype_data)
 
         self.model = translator.model
-        self.ptype = translator.ptype()
+        self.ptype = translator.construct_ptype()
         self.model_name = model_name
-        self.description = description if description else translator.create_description()
+        self.description = description if description else translator.describe()
         self.versioned = versioned
-        self.metadata = translator.vetiver_create_meta(
+        self.metadata = translator.create_meta(
             metadata, required_pkgs=["vetiver"]
         )
         self.handler_predict = translator.handler_predict