mlflow--mlflow
wehub-resource-sync
132 行
4.8 KiB
Python
132 行
4.8 KiB
Python
import json
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import pathlib
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import pickle
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import numpy as np
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import pandas as pd
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import pytest
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from matplotlib.figure import Figure
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from mlflow.exceptions import MlflowException
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from mlflow.models.evaluation.artifacts import (
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CsvEvaluationArtifact,
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ImageEvaluationArtifact,
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JsonEvaluationArtifact,
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NumpyEvaluationArtifact,
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ParquetEvaluationArtifact,
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PickleEvaluationArtifact,
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TextEvaluationArtifact,
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_infer_artifact_type_and_ext,
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)
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from mlflow.models.evaluation.default_evaluator import _CustomArtifact
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@pytest.fixture
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def cm_fn_tuple():
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return _CustomArtifact(lambda: None, "", 0, "")
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def __generate_dummy_json_file(path):
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with open(path, "w") as f:
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json.dump([1, 2, 3], f)
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class __DummyClass:
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def __init__(self):
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self.test = 1
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@pytest.mark.parametrize(
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("is_file", "artifact", "artifact_type", "ext"),
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[
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(True, lambda path: Figure().savefig(path), ImageEvaluationArtifact, "png"),
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(True, lambda path: Figure().savefig(path), ImageEvaluationArtifact, "jpg"),
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(True, lambda path: Figure().savefig(path), ImageEvaluationArtifact, "jpeg"),
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(True, __generate_dummy_json_file, JsonEvaluationArtifact, "json"),
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(True, lambda path: pathlib.Path(path).write_text("test"), TextEvaluationArtifact, "txt"),
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(
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True,
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lambda path: np.save(path, np.array([1, 2, 3]), allow_pickle=False),
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NumpyEvaluationArtifact,
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"npy",
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),
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(
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True,
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lambda path: pd.DataFrame({"test": [1, 2, 3]}).to_csv(path, index=False),
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CsvEvaluationArtifact,
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"csv",
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),
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(
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True,
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lambda path: pd.DataFrame({"test": [1, 2, 3]}).to_parquet(path),
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ParquetEvaluationArtifact,
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"parquet",
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),
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(False, pd.DataFrame({"test": [1, 2, 3]}), CsvEvaluationArtifact, "csv"),
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(False, np.array([1, 2, 3]), NumpyEvaluationArtifact, "npy"),
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(False, Figure(), ImageEvaluationArtifact, "png"),
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(False, {"a": 1, "b": "e", "c": 1.2, "d": [1, 2]}, JsonEvaluationArtifact, "json"),
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(False, [1, 2, 3, "test"], JsonEvaluationArtifact, "json"),
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(False, '{"a": 1, "b": [1.2, 3]}', JsonEvaluationArtifact, "json"),
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(False, '[1, 2, 3, "test"]', JsonEvaluationArtifact, "json"),
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(False, __DummyClass(), PickleEvaluationArtifact, "pickle"),
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],
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)
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def test_infer_artifact_type_and_ext(is_file, artifact, artifact_type, ext, tmp_path, cm_fn_tuple):
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if is_file:
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artifact_representation = tmp_path / f"test.{ext}"
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artifact(artifact_representation)
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else:
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artifact_representation = artifact
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inferred_from_path, inferred_type, inferred_ext = _infer_artifact_type_and_ext(
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f"{ext}_{artifact_type.__name__}_artifact", artifact_representation, cm_fn_tuple
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)
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assert not is_file ^ inferred_from_path
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assert inferred_type is artifact_type
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assert inferred_ext == f".{ext}"
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def test_infer_artifact_type_and_ext_raise_exception_for_non_file_non_json_str(cm_fn_tuple):
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with pytest.raises(
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MlflowException,
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match="with string representation 'some random str' that is "
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"neither a valid path to a file nor a JSON string",
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):
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_infer_artifact_type_and_ext("test_artifact", "some random str", cm_fn_tuple)
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def test_infer_artifact_type_and_ext_raise_exception_for_non_existent_path(tmp_path, cm_fn_tuple):
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path = tmp_path / "does_not_exist_path"
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with pytest.raises(MlflowException, match=f"with path '{path}' does not exist"):
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_infer_artifact_type_and_ext("test_artifact", path, cm_fn_tuple)
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def test_infer_artifact_type_and_ext_raise_exception_for_non_file_artifact(tmp_path, cm_fn_tuple):
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with pytest.raises(MlflowException, match=f"with path '{tmp_path}' is not a file"):
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_infer_artifact_type_and_ext("non_file_artifact", tmp_path, cm_fn_tuple)
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def test_infer_artifact_type_and_ext_raise_exception_for_unsupported_ext(tmp_path, cm_fn_tuple):
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path = tmp_path / "invalid_ext_example.some_ext"
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with open(path, "w") as f:
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f.write("some stuff that shouldn't be read")
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with pytest.raises(
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MlflowException,
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match=f"with path '{path}' does not match any of the supported file extensions",
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):
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_infer_artifact_type_and_ext("invalid_ext_artifact", path, cm_fn_tuple)
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def test_pickle_evaluation_artifact_load_raises_when_pickle_deserialization_disabled(
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tmp_path, monkeypatch
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):
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monkeypatch.setenv("MLFLOW_ALLOW_PICKLE_DESERIALIZATION", "false")
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artifact_path = tmp_path / "artifact.pickle"
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with open(artifact_path, "wb") as f:
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pickle.dump({"key": "value"}, f)
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artifact = PickleEvaluationArtifact(uri="test_uri")
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with pytest.raises(MlflowException, match="MLFLOW_ALLOW_PICKLE_DESERIALIZATION"):
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artifact._load_content_from_file(artifact_path)
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