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author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-04-09 11:25:39 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-04-09 11:25:39 -0700 |
commit | adb9d73cd6543c9edfc6b03a6d20061ff09c69f9 (patch) | |
tree | 153ca83019207ddf75e4bcdb07fe68fe0273e365 /examples/src/main/python/ml | |
parent | 1598d11bb0248384872cf88bc2b16f3b238046ad (diff) | |
download | spark-adb9d73cd6543c9edfc6b03a6d20061ff09c69f9.tar.gz spark-adb9d73cd6543c9edfc6b03a6d20061ff09c69f9.tar.bz2 spark-adb9d73cd6543c9edfc6b03a6d20061ff09c69f9.zip |
[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.
Diffstat (limited to 'examples/src/main/python/ml')
-rw-r--r-- | examples/src/main/python/ml/dct_example.py | 45 | ||||
-rw-r--r-- | examples/src/main/python/ml/max_abs_scaler_example.py | 43 | ||||
-rw-r--r-- | examples/src/main/python/ml/min_max_scaler_example.py | 43 |
3 files changed, 131 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/dct_example.py b/examples/src/main/python/ml/dct_example.py new file mode 100644 index 0000000000..264d47f404 --- /dev/null +++ b/examples/src/main/python/ml/dct_example.py @@ -0,0 +1,45 @@ +# +# 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() diff --git a/examples/src/main/python/ml/max_abs_scaler_example.py b/examples/src/main/python/ml/max_abs_scaler_example.py new file mode 100644 index 0000000000..d9b69eef1c --- /dev/null +++ b/examples/src/main/python/ml/max_abs_scaler_example.py @@ -0,0 +1,43 @@ +# +# 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() diff --git a/examples/src/main/python/ml/min_max_scaler_example.py b/examples/src/main/python/ml/min_max_scaler_example.py new file mode 100644 index 0000000000..2f8e4ade46 --- /dev/null +++ b/examples/src/main/python/ml/min_max_scaler_example.py @@ -0,0 +1,43 @@ +# +# 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() |