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_checkpoint(result.checkpoint)
assert model.n_jobs == 1
def test_tune(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",
params={"max_depth": 4},
cv=5,
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
)
tune.run(
trainer.as_trainable(),
config={"params": {
"max_depth": tune.randint(2, 4)
}},
num_samples=2,
metric="cv/test_score_mean",
mode="max",
)
# Make sure original Trainer is not affected.
assert trainer.params["max_depth"] == 4
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_validation(ray_start_4_cpus):
train_dataset = ray.data.from_pandas(train_df)
valid_dataset = ray.data.from_pandas(test_df)
with pytest.raises(KeyError, match=TRAIN_DATASET_KEY):
SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config=scale_config,
label_column="target",
datasets={"valid": valid_dataset},
)
with pytest.raises(KeyError, match="cv"):
SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config=scale_config,
label_column="target",
datasets={
TRAIN_DATASET_KEY: train_dataset,
"cv": valid_dataset
},
)
with pytest.raises(ValueError, match="are not allowed to be set"):
SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config={"num_workers": 2},
label_column="target",
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
)
with pytest.raises(ValueError, match="parallelize_cv"):
SklearnTrainer(
estimator=RandomForestClassifier(),
scaling_config={"trainer_resources": {
"GPU": 1
}},
label_column="target",
cv=5,
parallelize_cv=True,
datasets={
TRAIN_DATASET_KEY: train_dataset,
"valid": valid_dataset
},
)
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_checkpoint(resume_from)
assert hasattr(model, "feature_importances_")
assert preprocessor.is_same
assert preprocessor.fitted_