updates from review

Author: isabelizimm

docs/source/index.rst

@@ -5,6 +5,8 @@ You can use vetiver with:
 
 - `scikit-learn <https://scikit-learn.org/stable/>`_
 - `pytorch <https://pytorch.org/>`_
+- `statsmodels <https://www.statsmodels.org/>`_
+- `xgboost <https://xgboost.readthedocs.io/>`_
 
 You can install the released version of vetiver from `PyPI <https://pypi.org/project/vetiver/>`_:
 
@@ -65,6 +67,18 @@ Monitor
    ~pin_metrics
    ~plot_metrics
 
+Model Handlers
+==================
+.. autosummary::
+   :toctree: reference/
+   :caption: Monitor
+
+   ~BaseHandler
+   ~SKLearnHandler
+   ~TorchHandler
+   ~StatsmodelsHandler
+   ~XGBoostHandler
+
 Advanced Usage
 ==================
 .. toctree::

vetiver/handlers/sklearn.py

@@ -16,15 +16,12 @@ class SKLearnHandler(BaseHandler):
 
     model_class = staticmethod(lambda: sklearn.base.BaseEstimator)
 
-    def __init__(self, model, ptype_data):
-        super().__init__(model, ptype_data)
-
     def describe(self):
         """Create description for sklearn model"""
         desc = f"Scikit-learn {self.model.__class__} model"
         return desc
 
-    def construct_meta(
+    def create_meta(
         user: list = None,
         version: str = None,
         url: str = None,
@@ -54,17 +51,9 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
 
-        if check_ptype:
-            if isinstance(input_data, pd.DataFrame):
-                prediction = self.model.predict(input_data)
-            else:
-                prediction = self.model.predict([input_data])
-
-        # do not check ptype
-        else:
-            if not isinstance(input_data, list):
-                input_data = [input_data.split(",")]  # user delimiter ?
-
+        if not check_ptype or isinstance(input_data, pd.DataFrame):
             prediction = self.model.predict(input_data)
+        else:
+            prediction = self.model.predict([input_data])
 
         return prediction

vetiver/handlers/statsmodels.py

@@ -13,7 +13,7 @@
 class StatsmodelsHandler(BaseHandler):
     """Handler class for creating VetiverModels with statsmodels.
 
-    Parameters
+    Methods
     ----------
     model : statsmodels
         a trained and fit statsmodels model
@@ -58,12 +58,12 @@ def handler_predict(self, input_data, check_ptype):
         prediction
             Prediction from model
         """
-        if sm_exists:
-            if isinstance(input_data, (list, pd.DataFrame)):
-                prediction = self.model.predict(input_data)
-            else:
-                prediction = self.model.predict([input_data])
-        else:
+        if not sm_exists:
             raise ImportError("Cannot import `statsmodels`")
 
+        if isinstance(input_data, (list, pd.DataFrame)):
+            prediction = self.model.predict(input_data)
+        else:
+            prediction = self.model.predict([input_data])
+
         return prediction

vetiver/handlers/torch.py

@@ -21,9 +21,6 @@ class TorchHandler(BaseHandler):
 
     model_class = staticmethod(lambda: torch.nn.Module)
 
-    def __init__(self, model, ptype_data):
-        super().__init__(model, ptype_data)
-
     def describe(self):
         """Create description for torch model"""
         desc = f"Pytorch model of type {type(self.model)}"
@@ -58,17 +55,15 @@ def handler_predict(self, input_data, check_ptype):
         prediction
             Prediction from model
         """
-        if torch_exists:
-            if check_ptype:
-                input_data = np.array(input_data, dtype=np.array(self.ptype_data).dtype)
-                prediction = self.model(torch.from_numpy(input_data))
-
-            # do not check ptype
-            else:
-                input_data = torch.tensor(input_data)
-                prediction = self.model(input_data)
+        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)
+            prediction = self.model(torch.from_numpy(input_data))
 
+        # do not check ptype
         else:
-            raise ImportError("Cannot import `torch`.")
+            input_data = torch.tensor(input_data)
+            prediction = self.model(input_data)
 
         return prediction

vetiver/handlers/xgboost.py

@@ -21,9 +21,6 @@ class XGBoostHandler(BaseHandler):
 
     model_class = staticmethod(lambda: xgboost.Booster)
 
-    def __init__(self, model, ptype_data):
-        super().__init__(model, ptype_data)
-
     def describe(self):
         """Create description for xgboost model"""
         desc = f"XGBoost {self.model.__class__} model."
@@ -59,17 +56,16 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
 
-        if xgb_exists:
-            if not isinstance(input_data, xgboost.DMatrix):
-                if not isinstance(input_data, pd.DataFrame):
-                    try:
-                        input_data = pd.DataFrame(input_data)
-                    except ValueError:
-                        raise (f"Expected a dict or DataFrame, got {type(input_data)}")
-                input_data = xgboost.DMatrix(input_data)
-
-            prediction = self.model.predict(input_data)
-        else:
+        if not xgb_exists:
             raise ImportError("Cannot import `xgboost`")
 
+        if not isinstance(input_data, pd.DataFrame):
+            try:
+                input_data = pd.DataFrame(input_data)
+            except ValueError:
+                raise (f"Expected a dict or DataFrame, got {type(input_data)}")
+        input_data = xgboost.DMatrix(input_data)
+
+        prediction = self.model.predict(input_data)
+
         return prediction