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95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
# Copyright 2025 Collate
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# Licensed under the Collate Community License, Version 1.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/LICENSE
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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OpenMetadata MlModel mixin unit test — validates sklearn model → CreateMlModelRequest conversion
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"""
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from unittest.mock import patch
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import pandas as pd
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import sklearn.datasets as datasets # noqa: PLR0402
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
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OpenMetadataConnection,
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)
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from metadata.generated.schema.entity.services.mlmodelService import MlModelService
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from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
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OpenMetadataJWTClientConfig,
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)
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from metadata.generated.schema.type.basic import FullyQualifiedEntityName
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from metadata.ingestion.ometa.ometa_api import OpenMetadata
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server_config = OpenMetadataConnection(
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hostPort="http://localhost:8585/api",
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authProvider="openmetadata",
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securityConfig=OpenMetadataJWTClientConfig(
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jwtToken="eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
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),
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)
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metadata = OpenMetadata(server_config)
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class TestMlModelSklearn:
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"""
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Unit test for get_mlmodel_sklearn — validates that sklearn model metadata
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is correctly extracted into a CreateMlModelRequest without calling the server.
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"""
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def test_get_sklearn(self):
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iris = datasets.load_iris()
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df = pd.DataFrame(iris.data, columns=iris.feature_names)
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y = iris.target
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x_train, _, y_train, _ = train_test_split(df, y, test_size=0.25, random_state=70)
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dtree = DecisionTreeClassifier()
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dtree.fit(x_train, y_train)
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mock_service = MlModelService(
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id="85811038-099a-11ed-861d-0242ac120002",
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name="scikit-learn",
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fullyQualifiedName=FullyQualifiedEntityName("scikit-learn"),
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serviceType="Sklearn",
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connection={"config": {"type": "Sklearn"}},
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)
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with patch.object(OpenMetadata, "get_service_or_create", return_value=mock_service):
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request = metadata.get_mlmodel_sklearn(
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name="test-sklearn",
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model=dtree,
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description="Creating a test sklearn model",
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)
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assert request.name.root == "test-sklearn"
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assert request.algorithm == "DecisionTreeClassifier"
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assert request.description.root == "Creating a test sklearn model"
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assert request.service.root == "scikit-learn"
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feature_names = {feature.name.root for feature in request.mlFeatures}
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assert feature_names == {
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"sepal_length__cm_",
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"sepal_width__cm_",
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"petal_length__cm_",
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"petal_width__cm_",
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}
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param_names = {param.name for param in request.mlHyperParameters}
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assert "criterion" in param_names
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assert "max_depth" in param_names
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assert "random_state" in param_names
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criterion_param = next(param for param in request.mlHyperParameters if param.name == "criterion")
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assert criterion_param is not None
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assert criterion_param.value is not None
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