def test_predict_feature_columns():
preprocessor = DummyPreprocessor()
predictor = LightGBMPredictor(model=model, preprocessor=preprocessor)
data_batch = np.array([[1, 2, 7], [3, 4, 8], [5, 6, 9]])
predictions = predictor.predict(data_batch, feature_columns=[0, 1])
assert len(predictions) == 3
assert hasattr(predictor.get_preprocessor(), "_batch_transformed")
def test_predict():
preprocessor = DummyPreprocessor()
predictor = LightGBMPredictor(model=model, preprocessor=preprocessor)
data_batch = np.array([[1, 2], [3, 4], [5, 6]])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
assert hasattr(predictor.preprocessor, "_batch_transformed")
def test_predict(batch_type):
preprocessor = DummyPreprocessor()
predictor = LightGBMPredictor(model=model, preprocessor=preprocessor)
raw_batch = pd.DataFrame([[1, 2], [3, 4], [5, 6]])
data_batch = convert_pandas_to_batch_type(raw_batch,
type=TYPE_TO_ENUM[batch_type])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
assert hasattr(predictor.get_preprocessor(), "_batch_transformed")
def test_predict_feature_columns_pandas():
pandas_data = pd.DataFrame(dummy_data, columns=["A", "B"])
pandas_target = pd.Series(dummy_target)
pandas_model = (lgbm.LGBMClassifier(n_estimators=10).fit(
pandas_data, pandas_target).booster_)
preprocessor = DummyPreprocessor()
predictor = LightGBMPredictor(model=pandas_model,
preprocessor=preprocessor)
data_batch = pd.DataFrame(np.array([[1, 2, 7], [3, 4, 8], [5, 6, 9]]),
columns=["A", "B", "C"])
predictions = predictor.predict(data_batch, feature_columns=["A", "B"])
assert len(predictions) == 3
assert hasattr(predictor.get_preprocessor(), "_batch_transformed")
def test_predict_no_preprocessor_no_training():
with tempfile.TemporaryDirectory() as tmpdir:
checkpoint = LightGBMCheckpoint.from_model(booster=model, path=tmpdir)
predictor = LightGBMPredictor.from_checkpoint(checkpoint)
data_batch = np.array([[1, 2], [3, 4], [5, 6]])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
def test_init():
preprocessor = DummyPreprocessor()
preprocessor.attr = 1
predictor = LightGBMPredictor(model=model, preprocessor=preprocessor)
with tempfile.TemporaryDirectory() as tmpdir:
# This somewhat convoluted procedure is the same as in the
# Trainers. The reason for saving model to disk instead
# of directly to the dict as bytes is due to all callbacks
# following save to disk logic. GBDT models are small
# enough that IO should not be an issue.
model.save_model(os.path.join(tmpdir, MODEL_KEY))
save_preprocessor_to_dir(preprocessor, tmpdir)
checkpoint = Checkpoint.from_directory(tmpdir)
checkpoint_predictor = LightGBMPredictor.from_checkpoint(checkpoint)
assert get_num_trees(checkpoint_predictor.model) == get_num_trees(
predictor.model)
assert checkpoint_predictor.preprocessor.attr == predictor.preprocessor.attr