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49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import datahub.emitter.mce_builder as builder
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import datahub.metadata.schema_classes as models
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from datahub.emitter.mcp import MetadataChangeProposalWrapper
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from datahub.emitter.rest_emitter import DatahubRestEmitter
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# Create an emitter to DataHub over REST
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emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
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model_urn = builder.make_ml_model_urn(
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model_name="my-recommendations-model-run-1", platform="science", env="PROD"
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)
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model_group_urns = [
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builder.make_ml_model_group_urn(
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group_name="my-recommendations-model-group", platform="science", env="PROD"
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)
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]
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feature_urns = [
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builder.make_ml_feature_urn(
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feature_name="user_signup_date", feature_table_name="users_feature_table"
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),
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builder.make_ml_feature_urn(
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feature_name="user_last_active_date", feature_table_name="users_feature_table"
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),
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]
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metadata_change_proposal = MetadataChangeProposalWrapper(
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entityType="mlModel",
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changeType=models.ChangeTypeClass.UPSERT,
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entityUrn=model_urn,
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aspectName="mlModelProperties",
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aspect=models.MLModelPropertiesClass(
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description="my feature",
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groups=model_group_urns,
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mlFeatures=feature_urns,
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trainingMetrics=[
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models.MLMetricClass(
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name="accuracy", description="accuracy of the model", value="1.0"
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)
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],
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hyperParams=[
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models.MLHyperParamClass(
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name="hyper_1", description="hyper_1", value="0.102"
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)
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],
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),
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)
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# Emit metadata!
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emitter.emit(metadata_change_proposal)
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