def test_no_auto_cpu_params(ray_start_4_cpus, tmpdir):
train_dataset = ray.data.from_pandas(train_df)
valid_dataset = ray.data.from_pandas(test_df)
class DummyPreprocessor(Preprocessor):
def __init__(self):
super().__init__()
self.is_same = True
def fit(self, dataset):
self.fitted_ = True
def _transform_pandas(self, df: "pd.DataFrame") -> "pd.DataFrame":
return df
trainer = SklearnTrainer(
estimator=RandomForestClassifier(n_jobs=1),
scaling_config=scale_config,
label_column="target",
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
preprocessor=DummyPreprocessor(),
set_estimator_cpus=False,
)
result = trainer.fit()
model, _ = load_from_checkpoint(result.checkpoint)
assert model.n_jobs == 1
def test_fit(ray_start_4_cpus):
train_dataset = ray.data.from_pandas(train_df)
valid_dataset = ray.data.from_pandas(test_df)
trainer = SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config=scale_config,
label_column="target",
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
)
result = trainer.fit()
assert "valid" in result.metrics
assert "cv" not in result.metrics
def test_preprocessor_in_checkpoint(ray_start_4_cpus, tmpdir):
train_dataset = ray.data.from_pandas(train_df)
valid_dataset = ray.data.from_pandas(test_df)
class DummyPreprocessor(Preprocessor):
def __init__(self):
super().__init__()
self.is_same = True
def fit(self, dataset):
self.fitted_ = True
def _transform_pandas(self, df: "pd.DataFrame") -> "pd.DataFrame":
return df
trainer = SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config=scale_config,
label_column="target",
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
preprocessor=DummyPreprocessor(),
)
result = trainer.fit()
# Move checkpoint to a different directory.
checkpoint_dict = result.checkpoint.to_dict()
checkpoint = Checkpoint.from_dict(checkpoint_dict)
checkpoint_path = checkpoint.to_directory(tmpdir)
resume_from = Checkpoint.from_directory(checkpoint_path)
model, preprocessor = load_from_checkpoint(resume_from)
assert hasattr(model, "feature_importances_")
assert preprocessor.is_same
assert preprocessor.fitted_
def train_sklearn(num_cpus: int, use_gpu: bool = False) -> Result:
if use_gpu and not cuMLRandomForestClassifier:
raise RuntimeError(
"cuML must be installed for GPU enabled sklearn estimators.")
train_dataset, valid_dataset, _ = prepare_data()
# Scale some random columns
columns_to_scale = ["mean radius", "mean texture"]
preprocessor = Chain(OrdinalEncoder(["categorical_column"]),
StandardScaler(columns=columns_to_scale))
if use_gpu:
trainer_resources = {"CPU": 1, "GPU": 1}
estimator = cuMLRandomForestClassifier()
else:
trainer_resources = {"CPU": num_cpus}
estimator = RandomForestClassifier()
trainer = SklearnTrainer(
estimator=estimator,
label_column="target",
datasets={
"train": train_dataset,
"valid": valid_dataset
},
preprocessor=preprocessor,
cv=5,
scaling_config={
"trainer_resources": trainer_resources,
},
)
result = trainer.fit()
print(result.metrics)
return result