examples/src/main/python/ml/pipeline_example.py - master - githubmirror/spark

examples/src/main/python/ml/pipeline_example.py
73 строки3 KB
Tian Gao
[SPARK-55355][PYTHON] Upgrade mypy version to the latest
10 фев 2026, 02:29
10 фев 2026, 02:29b4b8165
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#

"""
Pipeline Example.
"""

# $example on$
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
# $example off$
from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("PipelineExample")\
        .getOrCreate()

    # $example on$
    # Prepare training documents from a list of (id, text, label) tuples.
    training = spark.createDataFrame([
        (0, "a b c d e spark", 1.0),
        (1, "b d", 0.0),
        (2, "spark f g h", 1.0),
        (3, "hadoop mapreduce", 0.0)
    ], ["id", "text", "label"])

    # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
    tokenizer = Tokenizer(inputCol="text", outputCol="words")
    hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
    lr = LogisticRegression(maxIter=10, regParam=0.001)
    pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])

    # Fit the pipeline to training documents.
    model = pipeline.fit(training)

    # Prepare test documents, which are unlabeled (id, text) tuples.
    test = spark.createDataFrame([
        (4, "spark i j k"),
        (5, "l m n"),
        (6, "spark hadoop spark"),
        (7, "apache hadoop")
    ], ["id", "text"])

    # Make predictions on test documents and print columns of interest.
    prediction = model.transform(test)
    selected = prediction.select("id", "text", "probability", "prediction")
    for row in selected.collect():
        rid, text, prob, prediction = row
        print(
            "(%d, %s) --> prob=%s, prediction=%f" % (
                rid, text, str(prob), prediction
            )
        )
    # $example off$

    spark.stop()