Cleaning

.github/workflows/precommit.yml

@@ -0,0 +1,18 @@
+name: Code Checks
+
+on:
+  workflow_dispatch:
+  push:
+    branches: ['main']
+  pull_request:
+  release:
+    types: [published]
+
+jobs:
+  pre-commit:
+    name: "Run pre-commit"
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+      - uses: pre-commit/action@v2.0.3

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 
 
-exclude: "(.*\\.csv)|(^pins/tests/_snapshots)"
+exclude: "(.*\\.csv)|(^examples/)|(^vetiver/tests/snapshots/)"
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.4.0

Makefile

@@ -20,6 +20,7 @@ help:
 	@echo "clean-test - remove test and coverage artifacts"
 	@echo "lint - check style with flake8"
 	@echo "test - run tests quickly with the default Python"
+	@echo "test-rsc - run tests for rsconnect"
 	@echo "coverage - check code coverage quickly with the default Python"
 	@echo "docs - generate Sphinx HTML documentation, including API docs"
 	@echo "cdocs - cleanout previous build & generate Sphinx HTML documentation, including API docs"
@@ -54,6 +55,9 @@ lint:
 	flake8 vetiver
 
 test: clean-test
+	pytest -m 'not rsc_test'
+
+test-rsc: clean-test
 	pytest
 
 coverage:

examples/coffeeratings.py

@@ -6,18 +6,32 @@
 from pathlib import Path
 
 # Load training data
-raw = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-07/coffee_ratings.csv')
+raw = pd.read_csv(
+    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-07/coffee_ratings.csv"
+)
 df = pd.DataFrame(raw)
-coffee = df[["total_cup_points", "aroma", "flavor", "sweetness", "acidity", \
-    "body", "uniformity", "balance"]].dropna()
-
-X_train, X_test, y_train, y_test = model_selection.train_test_split(coffee.iloc[:,1:],coffee['total_cup_points'],test_size=0.2)
+coffee = df[
+    [
+        "total_cup_points",
+        "aroma",
+        "flavor",
+        "sweetness",
+        "acidity",
+        "body",
+        "uniformity",
+        "balance",
+    ]
+].dropna()
+
+X_train, X_test, y_train, y_test = model_selection.train_test_split(
+    coffee.iloc[:, 1:], coffee["total_cup_points"], test_size=0.2
+)
 
 # fit model
 lr_fit = LinearRegression().fit(X_train, y_train)
 
 # create vetiver model
-v = vetiver.VetiverModel(lr_fit, ptype_data=X_train, model_name = "v")
+v = vetiver.VetiverModel(lr_fit, ptype_data=X_train, model_name="v")
 
 # version model via pin
 from pins import board_folder
@@ -27,12 +41,12 @@
 model_board = board_folder(path=path, versioned=True, allow_pickle_read=True)
 vetiver_pin_write(board=model_board, model=v)
 
-myapp = vetiver.VetiverAPI(v, check_ptype = True)
+myapp = vetiver.VetiverAPI(v, check_ptype=True)
 api = myapp.app
 
 # next, run myapp.run() to start API and see visual documentation
 # create app.py file that includes pinned VetiverAPI to be deployed
-vetiver.vetiver_write_app(model_board, "v", file = path+"app.py")
+vetiver.vetiver_write_app(model_board, "v", file=path + "app.py")
 
 # automatically create requirements.txt
 vetiver.load_pkgs(model=v, path=path)

examples/coffeeratings/app.py

@@ -2,8 +2,8 @@
 import pins
 
 # edited to reflect Docker container path, and allow_pickle_read=True
-b = pins.board_folder('/code/app', allow_pickle_read=True)
-v = vetiver.vetiver_pin_read(b, 'v', version = '20220415T174503Z-06d9b')
+b = pins.board_folder("/code/app", allow_pickle_read=True)
+v = vetiver.vetiver_pin_read(b, "v", version="20220415T174503Z-06d9b")
 
 vetiver_api = vetiver.VetiverAPI(v)
 api = vetiver_api.app

examples/coffeeratings/vetiver_requirements.txt

@@ -72,4 +72,4 @@ uvicorn==0.17.6
     # via vetiver
 
 # manually edited to reflect appropriate versions of packages currently being developed
-git+https://github.com/isabelizimm/vetiver-python.git@37dff15a63238b1e6c44788af70473f19c29d83e
+git+https://github.com/isabelizimm/vetiver-python.git@37dff15a63238b1e6c44788af70473f19c29d83e

vetiver/__init__.py

@@ -14,8 +14,8 @@
 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
-from .monitor import compute_metrics, pin_metrics, plot_metrics, _rolling_df # noqa
+from .rsconnect import deploy_rsconnect  # noqa
+from .monitor import compute_metrics, pin_metrics, plot_metrics, _rolling_df  # noqa
 
 __author__ = "Isabel Zimmerman <isabel.zimmerman@rstudio.com>"
 __all__ = []

vetiver/data/__init__.py

@@ -12,6 +12,7 @@ def __dir__():
 
 def __getattr__(k):
     import pandas as pd
+
     f_path = sources.get("mtcars")
     if k == "chicago":
         f_path = sources.get("chicago")

vetiver/pin_read_write.py

@@ -61,12 +61,14 @@ def vetiver_pin_read(board, name: str, version: str = None) -> VetiverModel:
 
     Notes
     -----
-    If reading a board from RSConnect, the `board` argument must be in "username/modelname" format.
+    If reading a board from RSConnect, the `board` argument must be in
+    "username/modelname" format.
 
     """
 
     warnings.warn(
-        "vetiver_pin_read will be removed in v1.0.0. Use classmethod VetiverModel.from_pin() instead",
+        "vetiver_pin_read will be removed in v1.0.0. Use classmethod "
+        "VetiverModel.from_pin() instead",
         DeprecationWarning,
     )
 

vetiver/ptype.py

@@ -19,7 +19,7 @@ class NoAvailablePTypeError(Exception):
 
     def __init__(
         self,
-        message="There is no method available to create a 0-row input data prototype for `model`",
+        message="There is no method to create a 0-row input data prototype for `model`",
     ):
         self.message = message
         super().__init__(self.message)

vetiver/rsconnect.py

@@ -26,16 +26,16 @@ def deploy_rsconnect(
 
     Parameters
     ----------
-        connect_server: 
+        connect_server:
             RSConnect Server
-        board: 
+        board:
             Pins board
         pin_name: str
             Name of pin
         version: str
             Version of pin
         extra_files: typing.List[str]
-            Any extra files to include in 
+            Any extra files to include in
         new:
             Force as a new deploy
         app_id:
@@ -46,7 +46,7 @@ def deploy_rsconnect(
             Optional name of a Python executable
         conda_mode: bool
             Use conda to build an environment.yml
-        force_generate: bool    
+        force_generate: bool
             Force generating "requirements.txt" or "environment.yml"
         log_callback: typing.Callable
             Callback to use to write the log to
@@ -64,11 +64,10 @@ def deploy_rsconnect(
                 shutil.copyfile(file, os.path.join(temp, filename))
                 new_files = new_files + [os.path.join(temp, filename)]
             extra_files = new_files
-        
+
         if board.fs.protocol == "file":
             shutil.copytree(board.path_to_pin(pin_name), os.path.join(temp, pin_name))
 
-
         tmp_app = temp + "/app.py"
 
         write_app(

vetiver/tests/test_build_vetiver_model.py

@@ -1,4 +1,3 @@
-import pytest
 import sklearn
 
 import vetiver as vt
@@ -74,7 +73,7 @@ def test_vetiver_model_no_ptype():
     )
 
     assert vt4.model == model
-    assert vt4.ptype == None
+    assert vt4.ptype is None
 
 
 def test_vetiver_model_from_pin():

vetiver/tests/test_rsconnect.py

@@ -4,8 +4,6 @@
 from pins.boards import BoardRsConnect
 from pins.rsconnect.api import RsConnectApi
 from pins.rsconnect.fs import RsConnectFs
-from vetiver import VetiverModel, vetiver_pin_write, mock
-import sklearn
 from rsconnect.api import RSConnectServer
 
 import vetiver
@@ -25,6 +23,7 @@ def server_from_key(name):
         api_key = json.load(f)[name]
         return RSConnectServer(RSC_SERVER_URL, api_key)
 
+
 def rsc_from_key(name):
     with open(RSC_KEYS_FNAME) as f:
         api_key = json.load(f)[name]
@@ -60,14 +59,14 @@ def rsc_short():
     rsc_delete_user_content(fs_susan.api)
 
 
-
 def test_board_pin_write(rsc_short):
     v = vetiver.VetiverModel(
         model=model, ptype_data=X_df, model_name="susan/model", versioned=None
     )
     vetiver.vetiver_pin_write(board=rsc_short, model=v)
     assert isinstance(rsc_short.pin_read("susan/model"), sklearn.dummy.DummyRegressor)
 
+
 @pytest.mark.xfail
 def test_deploy(rsc_short):
     # TODO: test in Dockerfile
@@ -76,11 +75,9 @@ def test_deploy(rsc_short):
     )
 
     vetiver.vetiver_pin_write(board=rsc_short, model=v)
-    
+
     vetiver.deploy_rsconnect(
-        connect_server=server_from_key("susan"), 
-        board=rsc_short, 
-        pin_name="susan/model"
+        connect_server=server_from_key("susan"), board=rsc_short, pin_name="susan/model"
     )
     response = vetiver.predict(RSC_SERVER_URL + "/predict/", json=X_df)
     assert response.status_code == 200, response.text

vetiver/tests/test_write_docker.py

@@ -1,4 +1,3 @@
-import pytest
 import os
 import sys
 import vetiver

vetiver/utils.py

@@ -3,7 +3,7 @@
 
 no_notebook = False
 try:
-    from IPython import get_ipython
+    from IPython import get_ipython  # noqa
 except ImportError:
     no_notebook = True
 
@@ -12,7 +12,8 @@ def _jupyter_nb():
 
     if not no_notebook:
         warnings.warn(
-            "WARNING: Jupyter Notebooks are not considered stable environments for production code"
+            "WARNING: Jupyter Notebooks are not considered stable environments "
+            "for production code"
         )
         nest_asyncio.apply()
     else:

vetiver/write_docker.py

@@ -45,7 +45,7 @@ def write_docker(
     py_version = str(sys.version_info.major) + "." + str(sys.version_info.minor)
 
     if rspm_env:
-        rspm = "\nRUN pip config set global.index-url https://colorado.rstudio.com/rspm/pypi/latest/simple"
+        rspm = "\nRUN pip config set global.index-url https://colorado.rstudio.com/rspm/pypi/latest/simple"  # noqa
     else:
         rspm = ""