def standardScaler(self):
from pyspark.ml.feature import StandardScaler
dataFrame = self.session.read.format("libsvm").load(
self.dataDir + "/data/mllib/sample_libsvm_data.txt")
scaler = StandardScaler(inputCol="features",
outputCol="scaledFeatures",
withStd=True,
withMean=False)
scalerModel = scaler.fit(dataFrame)
scaledData = scalerModel.transform(dataFrame)
scaledData.show()
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)
print("Features scaled to range: [%f, %f]" %
(scaler.getMin(), scaler.getMax()))
scaledData.select("features", "scaledFeatures").show()
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.select("features", "scaledFeatures").show()
