mirror of
https://github.com/open-metadata/OpenMetadata.git
synced 2025-11-04 12:36:23 +00:00
35efd49256
* Added control for DBT descriptions * Fixed tests * Added UI changes * fixed maven ci tests * Java formatting changes * ui review fixes * Fixed pytests * Fixed python integration tests * fixed airflow tests Co-authored-by: Onkar Ravgan <onkarravgan@Onkars-MacBook-Pro.local>
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# Copyright 2021 Collate
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""
|
|
OpenMetadata MlModel mixin test
|
|
"""
|
|
from unittest import TestCase
|
|
|
|
import pandas as pd
|
|
import sklearn.datasets as datasets
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.tree import DecisionTreeClassifier
|
|
|
|
from metadata.generated.schema.api.data.createMlModel import CreateMlModelRequest
|
|
from metadata.generated.schema.entity.data.mlmodel import MlModel
|
|
from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
|
|
OpenMetadataConnection,
|
|
)
|
|
from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
|
|
OpenMetadataJWTClientConfig,
|
|
)
|
|
from metadata.ingestion.ometa.ometa_api import OpenMetadata
|
|
|
|
|
|
class OMetaModelMixinTest(TestCase):
|
|
"""
|
|
Test the MlModel integrations from MlModel Mixin
|
|
"""
|
|
|
|
server_config = OpenMetadataConnection(
|
|
hostPort="http://localhost:8585/api",
|
|
authProvider="openmetadata",
|
|
securityConfig=OpenMetadataJWTClientConfig(
|
|
jwtToken="eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
|
|
),
|
|
)
|
|
metadata = OpenMetadata(server_config)
|
|
|
|
iris = datasets.load_iris()
|
|
|
|
def test_get_sklearn(self):
|
|
"""
|
|
Check that we can ingest an SKlearn model
|
|
"""
|
|
df = pd.DataFrame(self.iris.data, columns=self.iris.feature_names)
|
|
y = self.iris.target
|
|
|
|
x_train, x_test, y_train, y_test = train_test_split(
|
|
df, y, test_size=0.25, random_state=70
|
|
)
|
|
|
|
dtree = DecisionTreeClassifier()
|
|
dtree.fit(x_train, y_train)
|
|
|
|
entity_create: CreateMlModelRequest = self.metadata.get_mlmodel_sklearn(
|
|
name="test-sklearn",
|
|
model=dtree,
|
|
description="Creating a test sklearn model",
|
|
)
|
|
|
|
entity: MlModel = self.metadata.create_or_update(data=entity_create)
|
|
|
|
self.assertEqual(entity.name, entity_create.name)
|
|
self.assertEqual(entity.algorithm, "DecisionTreeClassifier")
|
|
self.assertEqual(
|
|
{feature.name.__root__ for feature in entity.mlFeatures},
|
|
{
|
|
"sepal_length__cm_",
|
|
"sepal_width__cm_",
|
|
"petal_length__cm_",
|
|
"petal_width__cm_",
|
|
},
|
|
)
|
|
|
|
hyper_param = next(
|
|
iter(
|
|
param for param in entity.mlHyperParameters if param.name == "criterion"
|
|
),
|
|
None,
|
|
)
|
|
self.assertIsNotNone(hyper_param)
|