[SPARK-14512] [DOC] Add python example for QuantileDiscretizer
## What changes were proposed in this pull request? Add the missing python example for QuantileDiscretizer ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12281 from zhengruifeng/discret_pe.
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@ -1118,6 +1118,15 @@ for more details on the API.
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{% include_example java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java %}
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{% include_example java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [QuantileDiscretizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.QuantileDiscretizer)
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for more details on the API.
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{% include_example python/ml/quantile_discretizer_example.py %}
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</div>
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</div>
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</div>
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# Feature Selectors
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# Feature Selectors
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examples/src/main/python/ml/quantile_discretizer_example.py
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examples/src/main/python/ml/quantile_discretizer_example.py
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@ -0,0 +1,39 @@
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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# $example on$
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from pyspark.ml.feature import QuantileDiscretizer
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# $example off$
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from pyspark.sql import SparkSession
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if __name__ == "__main__":
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spark = SparkSession.builder.appName("PythonQuantileDiscretizerExample").getOrCreate()
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# $example on$
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data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
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dataFrame = spark.createDataFrame(data, ["id", "hour"])
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discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")
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result = discretizer.fit(dataFrame).transform(dataFrame)
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result.show()
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# $example off$
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spark.stop()
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