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Fix failing unit tests (#558)

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I updated the tree serialization format for the new scikit learn versions. I also updated the minimum requirement of scikit learn to 1.3 to ensure compatibility.

Fixes #555
This commit is contained in:
Valeriy Khakhutskyy 2023-07-10 15:15:58 +02:00 committed by GitHub
parent 5ac8a053f0
commit f38de0ed05
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 32 additions and 13 deletions
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23
eland/ml/exporters/_sklearn_deserializers.py
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@ -97,18 +97,23 @@ class Tree:
impurity[i], impurity[i],
n_node_samples[i], n_node_samples[i],
weighted_n_node_samples[i], weighted_n_node_samples[i],
True,
) )
for i in range(node_count) for i in range(node_count)
], ],
dtype=[ dtype={
("left_child", "<i8"), "names": [
("right_child", "<i8"), "left_child",
("feature", "<i8"), "right_child",
("threshold", "<f8"), "feature",
("impurity", "<f8"), "threshold",
("n_node_samples", "<i8"), "impurity",
("weighted_n_node_samples", "<f8"), "n_node_samples",
], "weighted_n_node_samples",
"missing_go_to_left",
],
"formats": ["<i8", "<i8", "<i8", "<f8", "<f8", "<i8", "<f8", "u1"],
},
) )
state = { state = {
"max_depth": self.max_depth, "max_depth": self.max_depth,

2
requirements-dev.txt
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@ -12,7 +12,7 @@ tqdm<5
# #
# Extras # Extras
# #
scikit-learn>=0.22.1,<2 scikit-learn>=1.3,<2
xgboost>=0.90,<2 xgboost>=0.90,<2
lightgbm>=2,<4 lightgbm>=2,<4

2
setup.py
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@ -56,7 +56,7 @@ with open(path.join(here, "README.md"), "r", "utf-8") as f:
extras = { extras = {
"xgboost": ["xgboost>=0.90,<2"], "xgboost": ["xgboost>=0.90,<2"],
"scikit-learn": ["scikit-learn>=0.22.1,<2"], "scikit-learn": ["scikit-learn>=1.3,<2"],
"lightgbm": ["lightgbm>=2,<4"], "lightgbm": ["lightgbm>=2,<4"],
"pytorch": [ "pytorch": [
"torch>=1.13.1,<2.0", "torch>=1.13.1,<2.0",

18
tests/ml/pytorch/test_pytorch_model_upload_pytest.py
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@ -103,17 +103,31 @@ def download_model_and_start_deployment(tmp_dir, quantize, model_id, task):
class TestPytorchModel: class TestPytorchModel:
def __init__(self):
# quantization does not work on ARM processors
# TODO: It seems that PyTorch 2.0 supports OneDNN for aarch64. We should
# revisit this when we upgrade to PyTorch 2.0.
import platform
self.quantize = (
True if platform.machine() not in ["arm64", "aarch64"] else False
)
@pytest.mark.parametrize("model_id,task,text_input,value", TEXT_PREDICTION_MODELS) @pytest.mark.parametrize("model_id,task,text_input,value", TEXT_PREDICTION_MODELS)
def test_text_prediction(self, model_id, task, text_input, value): def test_text_prediction(self, model_id, task, text_input, value):
with tempfile.TemporaryDirectory() as tmp_dir: with tempfile.TemporaryDirectory() as tmp_dir:
ptm = download_model_and_start_deployment(tmp_dir, True, model_id, task) ptm = download_model_and_start_deployment(
tmp_dir, self.quantize, model_id, task
)
result = ptm.infer(docs=[{"text_field": text_input}]) result = ptm.infer(docs=[{"text_field": text_input}])
assert result["predicted_value"] == value assert result["predicted_value"] == value
@pytest.mark.parametrize("model_id,task,text_input", TEXT_EMBEDDING_MODELS) @pytest.mark.parametrize("model_id,task,text_input", TEXT_EMBEDDING_MODELS)
def test_text_embedding(self, model_id, task, text_input): def test_text_embedding(self, model_id, task, text_input):
with tempfile.TemporaryDirectory() as tmp_dir: with tempfile.TemporaryDirectory() as tmp_dir:
ptm = download_model_and_start_deployment(tmp_dir, True, model_id, task) ptm = download_model_and_start_deployment(
tmp_dir, self.quantize, model_id, task
)
ptm.infer(docs=[{"text_field": text_input}]) ptm.infer(docs=[{"text_field": text_input}])
if ES_VERSION >= (8, 8, 0): if ES_VERSION >= (8, 8, 0):