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81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
# Copyright 2021 Collate
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# Licensed under the Apache License, Version 2.0 (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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# http://www.apache.org/licenses/LICENSE-2.0
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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 test
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"""
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from unittest import TestCase
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import pandas as pd
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import sklearn.datasets as datasets
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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.api.data.createMlModel import CreateMlModelRequest
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from metadata.generated.schema.entity.data.mlmodel import MlModel
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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.ingestion.ometa.ometa_api import OpenMetadata
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class OMetaModelMixinTest(TestCase):
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"""
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Test the MlModel integrations from MlModel Mixin
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"""
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server_config = OpenMetadataConnection(hostPort="http://localhost:8585/api")
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metadata = OpenMetadata(server_config)
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iris = datasets.load_iris()
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def test_get_sklearn(self):
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"""
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Check that we can ingest an SKlearn model
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"""
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df = pd.DataFrame(self.iris.data, columns=self.iris.feature_names)
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y = self.iris.target
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x_train, x_test, y_train, y_test = train_test_split(
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df, y, test_size=0.25, random_state=70
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)
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dtree = DecisionTreeClassifier()
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dtree.fit(x_train, y_train)
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entity_create: CreateMlModelRequest = self.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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entity: MlModel = self.metadata.create_or_update(data=entity_create)
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self.assertEqual(entity.name, entity_create.name)
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self.assertEqual(entity.algorithm, "DecisionTreeClassifier")
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self.assertEqual(
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{feature.name.__root__ for feature in entity.mlFeatures},
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{
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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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)
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hyper_param = next(
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iter(
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param for param in entity.mlHyperParameters if param.name == "criterion"
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),
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None,
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)
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self.assertIsNotNone(hyper_param)
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