refactor tests (#209)

* refactor tests

* parameterize ptype tests

vetiver/data/__init__.py

@@ -1,22 +1,24 @@
-from importlib_resources import files as _files
-
-sources = {
-    "mtcars": _files("vetiver") / "data/mtcars.csv",
-    "chicago": _files("vetiver") / "data/chicago.csv",
-    "sacramento": _files("vetiver") / "data/sacramento.csv",
-}
+__all__ = [
+    "mtcars",
+    "chicago",
+    "sacramento",
+]
 
 
 def __dir__():
-    return list(sources)
+    return __all__
 
 
-def __getattr__(k):
+def _load_data_csv(name):
     import pandas as pd
+    import pkg_resources
+
+    fname = pkg_resources.resource_filename("vetiver.data", f"{name}.csv")
+    return pd.read_csv(fname)
+
+
+def __getattr__(name):
+    if name not in __all__:
+        raise AttributeError(f"No dataset named: {name}")
 
-    f_path = sources.get("mtcars")
-    if k == "chicago":
-        f_path = sources.get("chicago")
-    elif k == "sacramento":
-        f_path = sources.get("sacramento")
-    return pd.read_csv(f_path)
+    return _load_data_csv(name)

vetiver/server.py

@@ -5,7 +5,7 @@
 import uvicorn
 import logging
 import pandas as pd
-from fastapi import FastAPI, Request, testclient
+from fastapi import FastAPI, Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.openapi.utils import get_openapi
 from fastapi.responses import HTMLResponse, RedirectResponse, PlainTextResponse
@@ -321,9 +321,8 @@ def predict(endpoint, data: Union[dict, pd.DataFrame, pd.Series], **kw) -> pd.Da
     >>> endpoint = vetiver.vetiver_endpoint(url='http://127.0.0.1:8000/predict')
     >>> vetiver.predict(endpoint, X)     # doctest: +SKIP
     """
-    if isinstance(endpoint, testclient.TestClient):
-        requester = endpoint
-        endpoint = requester.app.root_path
+    if "test_client" in kw:
+        requester = kw.pop("test_client")
     else:
         requester = requests
 

vetiver/tests/conftest.py

@@ -0,0 +1,30 @@
+import pytest
+from vetiver import VetiverModel, VetiverAPI
+from vetiver.helpers import api_data_to_frame
+from starlette.testclient import TestClient
+
+
+def sum_values(x):
+    return x.sum().to_list()
+
+
+def sum_values_no_prototype(x):
+    return api_data_to_frame(x).sum().to_list()
+
+
+@pytest.fixture
+def client(model: VetiverModel) -> TestClient:
+    app = VetiverAPI(model, check_prototype=True)
+    app.vetiver_post(sum_values, "sum")
+    client = TestClient(app.app)
+
+    return client
+
+
+@pytest.fixture
+def client_no_prototype(model: VetiverModel) -> TestClient:
+    app = VetiverAPI(model, check_prototype=False)
+    app.vetiver_post(sum_values_no_prototype, "sum")
+    client = TestClient(app.app)
+
+    return client

vetiver/tests/test_add_endpoint.py

@@ -1,75 +1,32 @@
 import pytest
-
-import numpy as np
 import pandas as pd
-from fastapi.testclient import TestClient
-
-from vetiver import mock, VetiverModel, VetiverAPI
-from vetiver.helpers import api_data_to_frame
-import vetiver
+from vetiver import mock, VetiverModel
 
 
-@pytest.fixture
-def vetiver_model():
-    np.random.seed(500)
+@pytest.fixture()
+def model():
     X, y = mock.get_mock_data()
-    model = mock.get_mock_model().fit(X, y)
-    v = VetiverModel(
-        model=model,
-        prototype_data=X,
-        model_name="my_model",
-        versioned=None,
-        description="A regression model for testing purposes",
-    )
-
-    return v
-
-
-def sum_values(x):
-    return x.sum().to_list()
-
-
-def sum_values_no_prototype(x):
-    return api_data_to_frame(x).sum().to_list()
-
+    model = mock.get_mock_model()
 
-@pytest.fixture
-def vetiver_client(vetiver_model):  # With check_prototype=True
-
-    app = VetiverAPI(vetiver_model, check_prototype=True)
-    app.vetiver_post(sum_values, "sum")
-
-    app.app.root_path = "/sum"
-    client = TestClient(app.app)
-
-    return client
+    return VetiverModel(model.fit(X, y), "model", prototype_data=X)
 
 
 @pytest.fixture
-def vetiver_client_check_ptype_false(vetiver_model):  # With check_prototype=False
-
-    app = VetiverAPI(vetiver_model, check_prototype=False)
-    app.vetiver_post(sum_values_no_prototype, "sum")
+def data() -> pd.DataFrame:
+    return pd.DataFrame({"B": [1, 1, 1], "C": [2, 2, 2], "D": [3, 3, 3]})
 
-    app.app.root_path = "/sum"
-    client = TestClient(app.app)
 
-    return client
-
-
-def test_endpoint_adds_ptype(vetiver_client):
-
-    data = pd.DataFrame({"B": [1, 1, 1], "C": [2, 2, 2], "D": [3, 3, 3]})
-    response = vetiver.predict(endpoint=vetiver_client, data=data)
+def test_endpoint_adds(client, data):
+    response = client.post("/sum/", data=data.to_json(orient="records"))
 
-    assert isinstance(response, pd.DataFrame)
-    assert response.to_json() == '{"sum":{"0":3,"1":6,"2":9}}', response.to_json()
+    assert response.status_code == 200
+    assert response.json() == {"sum": [3, 6, 9]}
 
 
-def test_endpoint_adds_no_ptype(vetiver_client_check_ptype_false):
+def test_endpoint_adds_no_prototype(client_no_prototype, data):
 
     data = pd.DataFrame({"B": [1, 1, 1], "C": [2, 2, 2], "D": [3, 3, 3]})
-    response = vetiver.predict(endpoint=vetiver_client_check_ptype_false, data=data)
+    response = client_no_prototype.post("/sum/", data=data.to_json(orient="records"))
 
-    assert isinstance(response, pd.DataFrame)
-    assert response.to_json() == '{"sum":{"0":3,"1":6,"2":9}}', response.to_json()
+    assert response.status_code == 200
+    assert response.json() == {"sum": [3, 6, 9]}