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38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import traceback
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from pyspark.ml.linalg import VectorUDT
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from azureml.core.model import Model
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from pyspark.ml.classification import LogisticRegressionModel
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from pyspark.sql.types import StructType, StructField
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from pyspark.sql.types import DoubleType
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from pyspark.sql import SQLContext
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from pyspark import SparkContext
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sc = SparkContext.getOrCreate()
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sqlContext = SQLContext(sc)
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spark = sqlContext.sparkSession
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input_schema = StructType([StructField("features", VectorUDT()), StructField("label", DoubleType())])
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reader = spark.read
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reader.schema(input_schema)
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def init():
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global model
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# note here "iris.model" is the name of the model registered under the workspace
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# this call should return the path to the model.pkl file on the local disk.
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model_path = Model.get_model_path('iris.model')
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# Load the model file back into a LogisticRegression model
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model = LogisticRegressionModel.load(model_path)
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def run(data):
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try:
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input_df = reader.json(sc.parallelize([data]))
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result = model.transform(input_df)
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# you can return any datatype as long as it is JSON-serializable
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return result.collect()[0]['prediction']
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except Exception as e:
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traceback.print_exc()
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error = str(e)
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return error
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