updates to be compliant with new fastapi and pydantic

All changes are internal. The main changes are in prototypes and API
errors. Prototypes now are created from dictionaries of tuples of (type, value). API errors are now a list of dictionaries, giving the type, location, error message, erroneous input, and a url to pydantic's website with more about the error.

Author: isabelizimm

vetiver/prototype.py

@@ -156,8 +156,10 @@ def _item(value):
 
     dict_data = dict(enumerate(data[0], 0))
     # pydantic requires strings as indicies
+    # if its a numpy type, we have to take the Python type due to Pydantic
+
     dict_data = {
-        f"{key}": (type(value), _item(value)) for key, value in dict_data.items()
+        f"{key}": (type(value.item()), _item(value)) for key, value in dict_data.items()
     }
     prototype = create_prototype(**dict_data)
     return prototype

vetiver/tests/test_build_vetiver_model.py

@@ -8,6 +8,9 @@
 import pandas as pd
 import pydantic
 import pins
+import numpy as np
+
+np.random.seed(50)
 
 # Load data, model
 X_df, y = get_mock_data()
@@ -33,8 +36,8 @@ def test_vetiver_model_array_prototype():
 
     assert vt1.model == model
     assert issubclass(vt1.prototype, vt.Prototype)
-    assert isinstance(vt1.prototype.construct(), pydantic.BaseModel)
-    assert list(vt1.prototype.__fields__.values())[0].type_ == int
+    assert isinstance(vt1.prototype.model_construct(), pydantic.BaseModel)
+    assert vt1.prototype.model_construct().__dict__ == {"0": 96, "1": 11, "2": 33}
 
 
 def test_vetiver_model_df_prototype():
@@ -48,8 +51,8 @@ def test_vetiver_model_df_prototype():
     )
 
     assert vt2.model == model
-    assert isinstance(vt2.prototype.construct(), pydantic.BaseModel)
-    assert list(vt2.prototype.__fields__.values())[0].type_ == int
+    assert isinstance(vt2.prototype.model_construct(), pydantic.BaseModel)
+    assert vt2.prototype.model_construct().B == 96
 
 
 def test_vetiver_model_dict_prototype():
@@ -64,8 +67,8 @@ def test_vetiver_model_dict_prototype():
     )
 
     assert vt3.model == model
-    assert isinstance(vt3.prototype.construct(), pydantic.BaseModel)
-    assert list(vt3.prototype.__fields__.values())[0].type_ == int
+    assert isinstance(vt3.prototype.model_construct(), pydantic.BaseModel)
+    assert vt3.prototype.model_construct().B == 0
 
 
 def test_vetiver_model_basemodel_prototype():
@@ -135,7 +138,7 @@ def test_vetiver_model_from_pin():
 
     assert isinstance(v2, vt.VetiverModel)
     assert isinstance(v2.model, sklearn.base.BaseEstimator)
-    assert isinstance(v2.prototype.construct(), pydantic.BaseModel)
+    assert isinstance(v2.prototype.model_construct(), pydantic.BaseModel)
     assert v2.metadata.user == {"test": 123}
     assert v2.metadata.version is not None
     assert v2.metadata.required_pkgs == ["scikit-learn"]
@@ -170,7 +173,7 @@ def test_vetiver_model_from_pin_user_metadata():
 
     assert isinstance(v2, vt.VetiverModel)
     assert isinstance(v2.model, sklearn.base.BaseEstimator)
-    assert isinstance(v2.prototype.construct(), pydantic.BaseModel)
+    assert isinstance(v2.prototype.model_construct(), pydantic.BaseModel)
     assert v2.metadata.user == custom_meta
     assert v2.metadata.version is not None
     assert v2.metadata.required_pkgs == loaded_pkgs

vetiver/tests/test_custom_handler.py

@@ -40,7 +40,7 @@ def test_custom_vetiver_model():
     assert v.description == "A  DummyRegressor model"
     assert not v.metadata.required_pkgs
     assert isinstance(v.model, sklearn.dummy.DummyRegressor)
-    assert isinstance(v.prototype.construct(), pydantic.BaseModel)
+    assert isinstance(v.prototype.model_construct(), pydantic.BaseModel)
 
 
 def test_custom_vetiver_model_no_ptype():
@@ -58,4 +58,4 @@ def test_custom_vetiver_model_no_ptype():
 
     assert v.description == "A regression model for testing purposes"
     assert isinstance(v.model, sklearn.dummy.DummyRegressor)
-    assert isinstance(v.prototype.construct(), pydantic.BaseModel)
+    assert isinstance(v.prototype.model_construct(), pydantic.BaseModel)

vetiver/tests/test_sklearn.py

@@ -73,12 +73,10 @@ def test_predict_sklearn_no_ptype(data, length, vetiver_client_check_ptype_false
 
 @pytest.mark.parametrize("data", [(0), 0, 0.0, "0"])
 def test_predict_sklearn_type_error(data, vetiver_client):
-    import re
 
-    msg = re.sub(
-        r"\n",
-        ": ",
-        "1 validation error for Request\nbody\n  value is not a valid list \(type=type_error.list\)",  # noqa
+    msg = str(
+        "[{'type': 'list_type', 'loc': ('body',), 'msg': 'Input should be a valid list', \
+        'input': '0', 'url': 'https://errors.pydantic.dev/2.0.3/v/list_type'}]"
     )
 
     with pytest.raises(TypeError, match=msg):

vetiver/vetiver_model.py

@@ -84,6 +84,7 @@ def __init__(
         translator = create_handler(model, prototype_data)
 
         self.model = translator.model
+        self.prototype_data = prototype_data
         self.prototype = translator.construct_prototype()
         self.model_name = model_name
         self.description = description if description else translator.describe()