def _train_pipeline(model: str, destination: str, ignore_prints: bool,
ignore_html: bool) -> None:
click.echo(f"Train and save pipeline in {destination}")
X, y = load_dataset()
train_pipeline(
X=X,
y=y,
model=model,
destination=destination,
ignore_prints=ignore_prints,
ignore_html=ignore_html,
)
def test_evaluate_pipeline(self):
runner = CliRunner()
pattern = "/*.joblib"
X, y = load_dataset()
dummy_pipeline = Pipeline(
[("dummy_classifier", DummyClassifier(strategy="constant", constant=0))]
)
with tempfile.TemporaryDirectory() as destination:
threshold = destination + "/DUMMY_threshold.json"
train_pipeline(
X=X,
y=y,
model="DUMMY",
pipeline=dummy_pipeline,
destination=destination,
ignore_prints=True,
ignore_html=True,
)
pipeline_path = glob.glob(destination + pattern)
runner.invoke(
main,
[
"evaluate",
"--pipeline",
pipeline_path[0],
"--threshold",
threshold,
"--prefix",
"DUMMY",
"--destination",
destination,
],
)
files = glob.glob(destination + "/*")
self.assertTrue(any([".png" in file for file in files]))
self.assertTrue(any([".json" in file for file in files]))
self.assertTrue(any([".csv" in file for file in files]))
def test_train_pipeline(self):
X, y = load_dataset()
pattern = "/*.joblib"
dummy_pipeline = Pipeline([("dummy_classifier",
DummyClassifier(strategy="constant",
constant=0))])
with tempfile.TemporaryDirectory() as destination:
train_pipeline(
X=X,
y=y,
model="XGBOOST",
destination=destination,
ignore_prints=True,
ignore_html=True,
)
train_pipeline(
X=X,
y=y,
model="RF",
destination=destination,
ignore_prints=True,
ignore_html=True,
)
train_pipeline(
X=X,
y=y,
model="DUMMY",
pipeline=dummy_pipeline,
destination=destination,
ignore_prints=True,
ignore_html=True,
)
files = glob.glob(destination + pattern)
self.assertTrue(any(["RF" in file for file in files]))
self.assertTrue(any(["XGBOOST" in file for file in files]))
self.assertTrue(any(["DUMMY" in file for file in files]))