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Diffstat (limited to 'examples/src/main/python/mllib/standard_scaler_example.py')
-rw-r--r-- | examples/src/main/python/mllib/standard_scaler_example.py | 55 |
1 files changed, 55 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/standard_scaler_example.py b/examples/src/main/python/mllib/standard_scaler_example.py new file mode 100644 index 0000000000..20a77a4708 --- /dev/null +++ b/examples/src/main/python/mllib/standard_scaler_example.py @@ -0,0 +1,55 @@ +# +# 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 +# $example on$ +from pyspark.mllib.feature import StandardScaler, StandardScalerModel +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.util import MLUtils +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="StandardScalerExample") # SparkContext + + # $example on$ + data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") + label = data.map(lambda x: x.label) + features = data.map(lambda x: x.features) + + scaler1 = StandardScaler().fit(features) + scaler2 = StandardScaler(withMean=True, withStd=True).fit(features) + + # data1 will be unit variance. + data1 = label.zip(scaler1.transform(features)) + + # Without converting the features into dense vectors, transformation with zero mean will raise + # exception on sparse vector. + # data2 will be unit variance and zero mean. + data2 = label.zip(scaler2.transform(features.map(lambda x: Vectors.dense(x.toArray())))) + # $example off$ + + print("data1:") + for each in data1.collect(): + print(each) + + print("data2:") + for each in data2.collect(): + print(each) + + sc.stop() |