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Commit adb9d73c authored by Zheng RuiFeng's avatar Zheng RuiFeng Committed by Xiangrui Meng
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[SPARK-14339][DOC] Add python examples for DCT,MinMaxScaler,MaxAbsScaler

## What changes were proposed in this pull request?
add three python examples

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12063 from zhengruifeng/dct_pe.
parent 1598d11b
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......@@ -413,6 +413,14 @@ for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [DCT Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.DCT)
for more details on the API.
{% include_example python/ml/dct_example.py %}
</div>
</div>
## StringIndexer
......@@ -771,6 +779,14 @@ for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [MinMaxScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MinMaxScaler)
for more details on the API.
{% include_example python/ml/min_max_scaler_example.py %}
</div>
</div>
......@@ -803,6 +819,14 @@ for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [MaxAbsScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MaxAbsScaler)
for more details on the API.
{% include_example python/ml/max_abs_scaler_example.py %}
</div>
</div>
## Bucketizer
......
#
# 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.
#
from __future__ import print_function
from pyspark import SparkContext
from pyspark.sql import SQLContext
# $example on$
from pyspark.ml.feature import DCT
from pyspark.mllib.linalg import Vectors
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="DCTExample")
sqlContext = SQLContext(sc)
# $example on$
df = sqlContext.createDataFrame([
(Vectors.dense([0.0, 1.0, -2.0, 3.0]),),
(Vectors.dense([-1.0, 2.0, 4.0, -7.0]),),
(Vectors.dense([14.0, -2.0, -5.0, 1.0]),)], ["features"])
dct = DCT(inverse=False, inputCol="features", outputCol="featuresDCT")
dctDf = dct.transform(df)
for dcts in dctDf.select("featuresDCT").take(3):
print(dcts)
# $example off$
sc.stop()
#
# 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.
#
from __future__ import print_function
from pyspark import SparkContext
from pyspark.sql import SQLContext
# $example on$
from pyspark.ml.feature import MaxAbsScaler
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="MaxAbsScalerExample")
sqlContext = SQLContext(sc)
# $example on$
dataFrame = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
scaler = MaxAbsScaler(inputCol="features", outputCol="scaledFeatures")
# Compute summary statistics and generate MaxAbsScalerModel
scalerModel = scaler.fit(dataFrame)
# rescale each feature to range [-1, 1].
scaledData = scalerModel.transform(dataFrame)
scaledData.show()
# $example off$
sc.stop()
#
# 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.
#
from __future__ import print_function
from pyspark import SparkContext
from pyspark.sql import SQLContext
# $example on$
from pyspark.ml.feature import MinMaxScaler
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="MinMaxScalerExample")
sqlContext = SQLContext(sc)
# $example on$
dataFrame = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
scaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures")
# Compute summary statistics and generate MinMaxScalerModel
scalerModel = scaler.fit(dataFrame)
# rescale each feature to range [min, max].
scaledData = scalerModel.transform(dataFrame)
scaledData.show()
# $example off$
sc.stop()
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