handlers to register themselves

.github/workflows/tests.yml

@@ -64,3 +64,20 @@ jobs:
       - name: Run tests
         run: |
           pytest vetiver -m 'rsc_test'
+
+  test-no-torch:
+    name: "Test no-torch"
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e .[dev]
+
+      - name: Run tests
+        run: |
+          pytest vetiver/tests/test_sklearn.py

docs/source/advancedusage/custom_handler.md

@@ -1,41 +1,45 @@
 # Custom Handlers
 
-There are two different ways that vetiver supports flexible handling for models that do not work automatically with the vetiver framework. The first way is with [new model types,](#new-model-type) where there is no current implementation for the type of model you would like to deploy. The second way is when you would like to implement a current handler, but [in a different way](#different-model-implementation). In either case, you *must* make a custom handler from the base `VetiverHandler`. A minimal custom handler could look like the following:
+There are two different ways that vetiver supports flexible handling for models that do not work automatically with the vetiver framework. The first way is with new model types where there is no current implementation for the type of model you would like to deploy. The second way is when you would like to implement a current handler, but in a different way. In either case, you should create a custom handler from vetiver's `BaseHandler()`. At a minimum, you must give the type of your model via `model_type` how predictions should be made, via the method `handler_predict()`. Then, initialize your handler with your model, and pass the object into `VetiverModel`.
+
+This example shows a custom handler of `newmodeltype` type.
 
 ```python
-from vetiver.handlers.base import VetiverHandler
+from vetiver.handlers.base import BaseHandler
 
-class SampleCustomHandler(VetiverHandler):
+class CustomHandler(BaseHandler):
     def __init__(model, ptype_data):
         super().__init__(model, ptype_data)
 
-    def handler_predict(self, input_data, check_ptype):
+    model_type = staticmethod(lambda: newmodeltype)
+
+    def handler_predict(self, input_data, check_ptype: bool):
         """
-        handler_predict defines how to make predictions from your model
+        Generates method for /predict endpoint in VetiverAPI
+
+        The `handler_predict` function executes at each API call. Use this
+        function for calling `predict()` and any other tasks that must be executed at each API call.
+
+        Parameters
+        ----------
+        input_data:
+            Test data
+        check_ptype: bool
+            Whether the ptype should be enforced
+
+        Returns
+        -------
+        prediction
+            Prediction from model
         """
         # your code here
-```
+        prediction = model.fancy_new_predict(input_data)
 
-## New model type
-If your model type is not supported by vetiver, you should create and then register the handler using [single dispatch](https://docs.python.org/3/library/functools.html#functools.singledispatch). Once the new type is registered, you are able to use `VetiverModel()` as normal. Here is a template for such a function:
-
-```python
-from vetiver.handlers._interface import create_handler
+        return prediction
 
-@create_handler.register
-def _(model: {_model_type}, ptype_data):
-    return SampleCustomHandler(model, ptype_data)
+new_model = CustomHandler(model, ptype_data)
 
-VetiverModel(your_model, "your_model")
+VetiverModel(new_model, "custom_model")
 ```
 
 If your model is a common type, please consider [submitting a pull request](https://github.com/rstudio/vetiver-python/pulls).
-
-## Different model implementation
-If your model's prediction function is different than vetiver's, you should create a custom handler with a `handler_predict` method to make predictions. Then, initialize your handler with your model, and pass the object into `VetiverModel`.
-
-```python
-new_model = SampleCustomHandler(your_model, your_ptype_data)
-
-VetiverModel(new_model, "your_model")
-```

vetiver/__init__.py

@@ -11,8 +11,7 @@
 from .meta import *  # noqa
 from .write_docker import write_docker  # noqa
 from .write_fastapi import write_app  # noqa
-from .handlers._interface import create_handler, InvalidModelError  # noqa
-from .handlers.base import VetiverHandler  # noqa
+from .handlers.base import BaseHandler, create_handler, InvalidModelError  # noqa
 from .handlers.sklearn import SKLearnHandler  # noqa
 from .handlers.torch import TorchHandler  # noqa
 from .rsconnect import deploy_rsconnect # noqa

vetiver/handlers/base.py

@@ -1,10 +1,62 @@
-from abc import ABCMeta
+from vetiver.handlers import base
+from functools import singledispatch
+from contextlib import suppress
 
 from ..ptype import vetiver_create_ptype
 from ..meta import _model_meta
 
 
-class VetiverHandler(metaclass=ABCMeta):
+class InvalidModelError(Exception):
+    """
+    Throw an error if `model` is not registered.
+    """
+
+    def __init__(
+        self,
+        message="The `model` argument must be a scikit-learn or torch model.",
+    ):
+        self.message = message
+        super().__init__(self.message)
+
+
+@singledispatch
+def create_handler(model, ptype_data):
+    """check for model type to handle prediction
+
+    Parameters
+    ----------
+    model: object
+        Description of parameter `x`.
+    ptype_data : object
+        An object with information (data) whose layout is to be determined.
+
+    Returns
+    -------
+    handler
+        Handler class for specified model type
+
+
+    Examples
+    --------
+    >>> import vetiver
+    >>> X, y = vetiver.mock.get_mock_data()
+    >>> model = vetiver.mock.get_mock_model()
+    >>> handler = vetiver.create_handler(model, X)
+    >>> handler.describe()
+    "Scikit-learn <class 'sklearn.dummy.DummyRegressor'> model"
+    """
+
+    raise InvalidModelError(
+        "Model must be an sklearn or torch model, or a \
+        custom handler must be used. See the docs for more info on custom handlers. \
+        https://rstudio.github.io/vetiver-python/advancedusage/custom_handler.html"
+    )
+
+
+# BaseHandler uses create_handler to register subclasses based on model_class
+
+
+class BaseHandler:
     """Base handler class for creating VetiverModel of different type.
 
     Parameters
@@ -15,6 +67,12 @@ class VetiverHandler(metaclass=ABCMeta):
         An object with information (data) whose layout is to be determined.
     """
 
+    @classmethod
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        with suppress(AttributeError, NameError):
+            create_handler.register(cls.model_class(), cls)
+
     def __init__(self, model, ptype_data):
         self.model = model
         self.ptype_data = ptype_data
@@ -79,3 +137,15 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
         ...
+
+
+# BaseHandler for subclassing, Handler for new model types
+Handler = BaseHandler
+
+
+@create_handler.register
+def _(model: base.BaseHandler, ptype_data):
+    if model.ptype_data is None and ptype_data is not None:
+        model.ptype_data = ptype_data
+
+    return model

vetiver/handlers/sklearn.py

@@ -2,10 +2,10 @@
 import sklearn
 
 from ..meta import _model_meta
-from .base import VetiverHandler
+from .base import BaseHandler
 
 
-class SKLearnHandler(VetiverHandler):
+class SKLearnHandler(BaseHandler):
     """Handler class for creating VetiverModels with sklearn.
 
     Parameters
@@ -14,7 +14,7 @@ class SKLearnHandler(VetiverHandler):
         a trained sklearn model
     """
 
-    base_class = sklearn.base.BaseEstimator
+    model_class = staticmethod(lambda: sklearn.base.BaseEstimator)
 
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
@@ -54,7 +54,7 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
 
-        if check_ptype == True:
+        if check_ptype:
             if isinstance(input_data, pd.DataFrame):
                 prediction = self.model.predict(input_data)
             else:

vetiver/handlers/torch.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from ..meta import _model_meta
-from .base import VetiverHandler
+from .base import BaseHandler
 
 torch_exists = True
 try:
@@ -10,7 +10,7 @@
     torch_exists = False
 
 
-class TorchHandler(VetiverHandler):
+class TorchHandler(BaseHandler):
     """Handler class for creating VetiverModels with torch.
 
     Parameters
@@ -19,7 +19,7 @@ class TorchHandler(VetiverHandler):
         a trained torch model
     """
 
-    base_class = torch.nn.Module
+    model_class = staticmethod(lambda: torch.nn.Module)
 
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
@@ -59,7 +59,7 @@ def handler_predict(self, input_data, check_ptype):
             Prediction from model
         """
         if torch_exists:
-            if check_ptype == True:
+            if check_ptype:
                 input_data = np.array(input_data, dtype=np.array(self.ptype_data).dtype)
                 prediction = self.model(torch.from_numpy(input_data))
 

vetiver/tests/test_custom_handler.py

@@ -2,13 +2,15 @@
 import pydantic
 import pandas as pd
 
-from vetiver import mock, VetiverModel, VetiverHandler
+from vetiver import mock, VetiverModel, BaseHandler
 
 
-class CustomHandler(VetiverHandler):
+class CustomHandler(BaseHandler):
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
+    model_type = staticmethod(lambda: sklearn.dummy.DummyRegressor)
+
     def handler_predict(self, input_data, check_ptype):
         if check_ptype is True:
             if isinstance(input_data, pd.DataFrame):